diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 8d32c85..1e91c17 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.7","generation_timestamp":"2024-11-30T02:44:14","documenter_version":"1.8.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.7","generation_timestamp":"2024-12-04T15:19:45","documenter_version":"1.8.0"}} \ No newline at end of file diff --git a/dev/api.html b/dev/api.html index d45d62a..2b60888 100644 --- a/dev/api.html +++ b/dev/api.html @@ -1,9 +1,9 @@ -API · BeforeIT.jl
GitHub

Code reference

In this page we document the functions which constitute the bulk of BeforeIT.jl functionality.

Agent types

BeforeIT.Aggregates — Type

This is a Aggregates type. It is used to store the aggregate variables of the economy. Note that t is an integer, while the rest are floats or vectors of floats.

Fields

  • Y [vector]: GDP data + predictions
  • pi_ [vector]: inflation data + predictions
  • P_bar: Global price index
  • P_bar_g [vector]: Producer price index for principal good g
  • P_bar_HH: Consumer price index
  • P_bar_CF: Capital price index
  • P_bar_h: CPI_h
  • P_bar_CF_h: Capital price index _h
  • Y_e: Expected GDP
  • gamma_e: Expected growth
  • pi_e: Expected inflation
  • t: Time index
source
BeforeIT.Bank — Type

This is a Bank type. It represents the bank of the model.

Fields

  • E_k: equity capital (common equity) of the bank
  • Pi_k: Profits of the bank
  • Pi_e_k: Expected profits of the bank
  • D_k: Residual and balancing item on the bank’s balance sheet
  • r: Rate for loans and morgages

Household fields (bank' owner)

  • Y_h: Net disposable income of bank owner (investor)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
  • K_h: Capital stock
  • D_h: Deposits
source
BeforeIT.CentralBank — Type

This is a CentralBank type. It represents the central bank of the model.

Fields

  • r_bar: Nominal interest rate
  • r_G: Interest rate on government bonds
  • rho: Parameter for gradual adjustment of the policy rate
  • r_star: Real equilibrium interest rate
  • pi_star: Inflation target by CB
  • xi_pi: Weight the CB puts on inflation targeting
  • xi_gamma: Weight placed on economic
  • E_CB: Central bank equity
source
BeforeIT.Firms — Type

This is a Firms type. Each field is an array which stores the values for all the firms in the economy. Note that the G_i, N_i and V_i fields are integers, while the rest are floats.

For all fields the entry at index i corresponds to the ith firm.

Fields

  • G_i: Principal product
  • alpha_bar_i: Average productivity of labor
  • beta_i: Productivity of intermediate consumption
  • kappa_i: Productivity of capital
  • w_i: Wages
  • w_bar_i: Average wage rate
  • delta_i: Depreciation rate for capital
  • tau_Y_i: Net tax rate on products
  • tau_K_i: Net tax rate on production
  • N_i: Number of persons employed
  • Y_i: Production of goods
  • Q_i: Sales of goods
  • Q_d_i: Demand for goods
  • P_i: Price
  • S_i: Inventories
  • K_i: Capital, in real terms
  • M_i: Intermediate goods/services and raw materials, in real terms
  • L_i: Outstanding loans
  • pi_bar_i: Operating margin
  • D_i: Deposits of the firm
  • Pi_i: Profits
  • V_i: Vacancies
  • I_i: Investments
  • E_i: Equity
  • P_bar_i: Price index
  • P_CF_i: Price index
  • DS_i: Differnece in stock of final goods
  • DM_i: Difference in stock of intermediate goods
  • DL_i: Obtained loans
  • DL_d_i: Target loans
  • K_e_i: Expected capital
  • L_e_i: Expected loans
  • Q_s_i: Expected sales
  • I_d_i: Desired investments
  • DM_d_i: Desired materials
  • N_d_i: Desired employment
  • Pi_e_i: Expected profits

Household fields (firms' owners)

  • Y_h: Net disposable income of firm owner (investor)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
  • K_h: Capital stock
  • D_h: Deposits of the owner of the firms
source
BeforeIT.Government — Type

This is a Government type. It represents the government of the model.

Fields

  • alpha_G: Autoregressive coefficient for government consumption
  • beta_G: Scalar constant for government consumption
  • sigma_G: Variance coefficient for government consumption
  • Y_G: Government revenues
  • C_G: Consumption demand of the general government
  • L_G: Loans taken out by the government
  • sb_inact: Social benefits for inactive persons
  • sb_other: Social benefits for all
  • C_d_j [vector]: Local governments consumption demand
  • C_j: Realised government consumption
  • P_j: Price inflation of government goods <- ??
source
BeforeIT.Model — Type

This is a Model type. It is used to store all the agents of the economy.

Fields

  • w_act: Workers that are active
  • w_inact: Workers that are inactive
  • firms: Firms
  • bank: Bank
  • cb: CentralBank
  • gov: Government
  • rotw: RestOfTheWorld
  • agg: Aggregates
source
BeforeIT.RestOfTheWorld — Type

This is a RestOfTheWorld type. It represents the rest of the world of the model.

Fields

  • alpha_E: Autoregressive coefficient for exports
  • beta_E: Scalar constant for exports
  • sigma_E: Variance coefficient for exports
  • alpha_I: Autoregressive coefficient for imports
  • beta_I: Scalar constant for imports
  • sigma_I: Variance coefficient for imports
  • Y_EA: GDP euro area
  • gamma_EA: Growth euro area
  • pi_EA: Inflation euro area
  • alpha_pi_EA: Autoregressive coefficient for euro area inflation
  • beta_pi_EA: Autoregressive coefficient for euro area inflation Scalar constant for euro area inflation
  • sigma_pi_EA: Variance coefficient for euro area inflation
  • alpha_Y_EA: Autoregressive coefficient for euro area GDP
  • beta_Y_EA: Autoregressive coefficient for euro area GDP Scalar constant for euro area GDP
  • sigma_Y_EA: Variance coefficient for euro area GDP
  • D_RoW: Net creditor/debtor position of the national economy to the rest of the world
  • Y_I: Supply of imports (in real terms)
  • C_E: Total demand for exports
  • C_d_l [vector]: Demand for exports of specific product
  • C_l: Realised consumption by foreign consumers
  • Y_m [vector]: Supply of imports per sector
  • Q_m [vector]: Sales for imports per sector
  • Q_d_m [vector]: Demand for goods
  • P_m [vector]: Price of imports per sector
  • P_l: Price inflation of exports <- ??
source
BeforeIT.Workers — Type

This is a Workers. Each field is an array which stores the values for all the workers in the economy. Note that the O_h field is an integer, while the rest are floats.

For all fields the entry at index i corresponds to the ith worker.

Fields

  • Y_h: Net disposable income of worker owner (investor)
  • D_h: Deposits
  • K_h: Capital stock
  • w_h: Wages (0 if inactive or unemployed)
  • O_h: Occupation (0 if unemployed, -1 if inactive)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
source

Initialisation function

BeforeIT.init_model — Function
init_model(parameters, initial_conditions, T, typeInt = Int64, typeFloat = Float64)

Initializes the model with given parameters and initial conditions.

Parameters:

  • parameters: A dictionary containing the model parameters.
  • initial_conditions: A dictionary containing the initial conditions.
  • T (integer): The time horizon of the model.
  • typeInt: (optional, default: Int64): The data type to be used for integer values.
  • typeFloat: (optional, default: Float64): The data type to be used for floating-point values.

Returns:

  • model::Model: The initialized model.
source
BeforeIT.update_variables_with_totals! — Method
update_variables_with_totals!(model::Model)

Update the variables in the given model with some global quantities obtained from all agents. This is the last step in the initialization process and it must be performed after all agents have been initialized.

Arguments

  • model::Model: The model object to update.

Returns

  • Nothing
source

Functions to run an entire simulation

BeforeIT.run_one_epoch! — Method
run_one_epoch!(model; multi_threading = false)

This function simulates a single epoch the economic model, updating various components of the model based the interactions between different economic agents. It accepts a model object, which encapsulates the state for the simulation, and an optional boolean parameter multi_threading to enable or disable multi-threading.

Key operations performed include:

  • Financial adjustments for firms and banks, including insolvency checks and profit calculations.
  • Economic expectations and adjustments, such as growth, inflation, and central bank rates.
  • Labor and credit market operations, including wage updates and loan processing.
  • Household economic activities, including consumption and investment budgeting.
  • Government and international trade financial activities, including budgeting and trade balances.
  • General market matching and accounting updates to reflect changes in economic indicators and positions.

The function updates the model in-place and does not return any value.

source
BeforeIT.run_n_sims — Method
run_n_sims(model, n_sims; shock = NoShock(), multi_threading = true)

A function that runs n_sims simulations in parallel with multiple threading and returns a vector of data objects of dimension n_sims.

Arguments

  • model: The model configuration used to simulate.
  • n_sims: The number of simulations to run in parallel.

Returns

  • data_vector: A vector containing the data objects collected during each simulation.
source
BeforeIT.run_one_sim! — Method
run_one_sim!(model; shock = NoShock())

Run a single simulation based on the provided model. The simulation runs for a number of epochs specified by model.prop.T.

Arguments

  • model::Model: The model configuration used for the simulation.

Returns

  • data::Data: The data collected during the simulation.

Details

The function initializes the data using BeforeIT.init_data(model), then iteratively updates the model and data for each epoch using BeforeIT.run_one_epoch!(model) and BeforeIT.update_data!(data, model) respectively.

Example

```julia model = BeforeIT.initializemodel(parameters, initialconditions, T) data = runonesim!(model)

source

Firms actions

BeforeIT.firms_deposits — Method
firms_deposits(firms, model)

Calculate the new deposits of firms.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • DD_i: Vector of new deposits

The new deposits DD_i are calculated as follows:

DD_i = sales + labour_cost + material_cost + taxes_products + taxes_production + corporate_tax + dividend_payments + interest_payments + interest_received + investment_cost + new_credit + debt_installment

where:

  • sales = P_i * Q_i
  • labour_cost = (1 + tau_SIF) * w_i * N_i * P_bar_HH
  • material_cost = -DM_i * P_bar_i
  • taxes_products = -tau_Y_i * P_i * Y_i
  • taxes_production = -tau_K_i * P_i * Y_i
  • corporate_tax = -tau_FIRM * pos(Pi_i)
  • dividend_payments = -theta_DIV * (1 - tau_FIRM) * pos(Pi_i)
  • interest_payments = -r * (L_i + pos(-D_i))
  • interest_received = r_bar * pos(D_i)
  • investment_cost = -P_CF_i * I_i
  • new_credit = DL_i
  • debt_installment = -theta * L_i
source
BeforeIT.firms_equity — Method
firms_equity(firms, model)

Calculate the equity of firms.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • E_i: Vector of equity

The equity E_i is calculated as follows:

\[E_i = D_i + M_i * \sum(a_{sg}[:, G_i] * \bar{P}_g) + P_i * S_i + \bar{P}_{CF} * K_i - L_i\]

where:

  • D_i: Deposits
  • M_i: Intermediate goods
  • a_sg: Technology coefficient of the gth product in the sth industry
  • G_i: Vector of goods
  • P_bar_g: Producer price index for principal good g
  • P_i: Price
  • S_i: Stock
  • P_bar_CF: Capital price index
  • K_i: Capital stock
  • L_i: Loans
source
BeforeIT.firms_expectations_and_decisions — Method
firms_expectations_and_decisions(firms, model)

Calculate the expectations and decisions of firms. That is: compute firm quantity, price, investment and intermediate-goods, employment decisions, expected profits, and desired/expected loans and capital.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • Q_s_i: Vector of desired quantities
  • I_d_i: Vector of desired investments
  • DM_d_i: Vector of desired intermediate goods
  • N_d_i: Vector of desired employment
  • Pi_e_i: Vector of expected profits
  • DL_d_i: Vector of desired new loans
  • K_e_i: Vector of expected capital
  • L_e_i: Vector of expected loans
  • P_i: Vector of prices
source
BeforeIT.firms_loans — Method
firms_loans(firms, model)

Calculate the new loans of firms.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • L_i: Vector of new loans

The new loans L_i are calculated as follows:

\[L_i = (1 - theta) * L_i + DL_i\]

where:

  • theta: Rate of repayment
  • L_i: Loans
  • DL_i: Acquired new loans
source
BeforeIT.firms_production — Method
firms_production(firms)

Calculate the production of firms.

Arguments

  • firms: Firms object

Returns

  • Y_i: Vector of production

The production Y_i is computed using a Leontief technology.

source
BeforeIT.firms_profits — Method
firms_profits(firms, model)

Calculate the profits of firms.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • Pi_i: Vector of profits

The profits Pi_i are calculated as follows:

Pi_i = in_sales + in_deposits - out_wages - out_expenses - out_depreciation - out_taxes_prods - out_taxes_capital - out_loans

where:

  • in_sales = P_i * Q_i + P_i * DS_i
  • in_deposits = r_bar * pos(D_i)
  • out_wages = (1 + tau_SIF) * w_i * N_i * P_bar_HH
  • out_expenses = 1 / beta_i * P_bar_i * Y_i
  • out_depreciation = delta_i / kappa_i * P_CF_i * Y_i
  • out_taxes_prods = tau_Y_i * P_i * Y_i
  • out_taxes_capital = tau_K_i * P_i * Y_i
  • out_loans = r * (L_i + pos(-D_i))
source
BeforeIT.firms_stocks — Method
firms_stocks(firms)

Calculate the stocks of firms.

Arguments

  • firms: Firms object

Returns

  • K_i: Vector of capital stock
  • M_i: Vector of intermediate goods
  • DS_i: Vector of differneces in stock of final goods
  • S_i: Vector of stock of final goods

The stocks are calculated as follows:

K_i = K_i - delta_i / kappa_i * Y_i + I_i
+API · BeforeIT.jl
GitHub
Code reference
In this page we document the functions which constitute the bulk of BeforeIT.jl functionality.
Agent types
BeforeIT.Aggregates — Type
This is a Aggregates type. It is used to store the aggregate variables of the economy. Note that t is an integer, while the rest are floats or vectors of floats.
Fields
  • Y [vector]: GDP data + predictions
  • pi_ [vector]: inflation data + predictions
  • P_bar: Global price index
  • P_bar_g [vector]: Producer price index for principal good g
  • P_bar_HH: Consumer price index
  • P_bar_CF: Capital price index
  • P_bar_h: CPI_h
  • P_bar_CF_h: Capital price index _h
  • Y_e: Expected GDP
  • gamma_e: Expected growth
  • pi_e: Expected inflation
  • t: Time index
source
BeforeIT.Bank — Type
This is a Bank type. It represents the bank of the model.
Fields
  • E_k: equity capital (common equity) of the bank
  • Pi_k: Profits of the bank
  • Pi_e_k: Expected profits of the bank
  • D_k: Residual and balancing item on the bank’s balance sheet
  • r: Rate for loans and morgages
Household fields (bank' owner)
  • Y_h: Net disposable income of bank owner (investor)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
  • K_h: Capital stock
  • D_h: Deposits
source
BeforeIT.CentralBank — Type
This is a CentralBank type. It represents the central bank of the model.
Fields
  • r_bar: Nominal interest rate
  • r_G: Interest rate on government bonds
  • rho: Parameter for gradual adjustment of the policy rate
  • r_star: Real equilibrium interest rate
  • pi_star: Inflation target by CB
  • xi_pi: Weight the CB puts on inflation targeting
  • xi_gamma: Weight placed on economic
  • E_CB: Central bank equity
source
BeforeIT.Firms — Type
This is a Firms type. Each field is an array which stores the values for all the firms in the economy. Note that the G_i, N_i and V_i fields are integers, while the rest are floats.
For all fields the entry at index i corresponds to the ith firm.
Fields
  • G_i: Principal product
  • alpha_bar_i: Average productivity of labor
  • beta_i: Productivity of intermediate consumption
  • kappa_i: Productivity of capital
  • w_i: Wages
  • w_bar_i: Average wage rate
  • delta_i: Depreciation rate for capital
  • tau_Y_i: Net tax rate on products
  • tau_K_i: Net tax rate on production
  • N_i: Number of persons employed
  • Y_i: Production of goods
  • Q_i: Sales of goods
  • Q_d_i: Demand for goods
  • P_i: Price
  • S_i: Inventories
  • K_i: Capital, in real terms
  • M_i: Intermediate goods/services and raw materials, in real terms
  • L_i: Outstanding loans
  • pi_bar_i: Operating margin
  • D_i: Deposits of the firm
  • Pi_i: Profits
  • V_i: Vacancies
  • I_i: Investments
  • E_i: Equity
  • P_bar_i: Price index
  • P_CF_i: Price index
  • DS_i: Differnece in stock of final goods
  • DM_i: Difference in stock of intermediate goods
  • DL_i: Obtained loans
  • DL_d_i: Target loans
  • K_e_i: Expected capital 
  • L_e_i: Expected loans
  • Q_s_i: Expected sales
  • I_d_i: Desired investments
  • DM_d_i: Desired materials
  • N_d_i: Desired employment
  • Pi_e_i: Expected profits
Household fields (firms' owners)
  • Y_h: Net disposable income of firm owner (investor)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
  • K_h: Capital stock
  • D_h: Deposits of the owner of the firms
source
BeforeIT.Government — Type
This is a Government type. It represents the government of the model.
Fields
  • alpha_G: Autoregressive coefficient for government consumption
  • beta_G: Scalar constant for government consumption
  • sigma_G: Variance coefficient for government consumption
  • Y_G: Government revenues
  • C_G: Consumption demand of the general government
  • L_G: Loans taken out by the government
  • sb_inact: Social benefits for inactive persons
  • sb_other: Social benefits for all
  • C_d_j [vector]: Local governments consumption demand
  • C_j: Realised government consumption
  • P_j: Price inflation of government goods <- ??
source
BeforeIT.Model — Type
This is a Model type. It is used to store all the agents of the economy.
Fields
  • w_act: Workers that are active
  • w_inact: Workers that are inactive
  • firms: Firms
  • bank: Bank
  • cb: CentralBank
  • gov: Government
  • rotw: RestOfTheWorld
  • agg: Aggregates
source
BeforeIT.RestOfTheWorld — Type
This is a RestOfTheWorld type. It represents the rest of the world of the model.
Fields
  • alpha_E: Autoregressive coefficient for exports
  • beta_E: Scalar constant for exports
  • sigma_E: Variance coefficient for exports
  • alpha_I: Autoregressive coefficient for imports
  • beta_I: Scalar constant for imports
  • sigma_I: Variance coefficient for imports
  • Y_EA: GDP euro area
  • gamma_EA: Growth euro area
  • pi_EA: Inflation euro area
  • alpha_pi_EA: Autoregressive coefficient for euro area inflation
  • beta_pi_EA: Autoregressive coefficient for euro area inflation Scalar constant for euro area inflation
  • sigma_pi_EA: Variance coefficient for euro area inflation
  • alpha_Y_EA: Autoregressive coefficient for euro area GDP
  • beta_Y_EA: Autoregressive coefficient for euro area GDP Scalar constant for euro area GDP
  • sigma_Y_EA: Variance coefficient for euro area GDP
  • D_RoW: Net creditor/debtor position of the national economy to the rest of the world
  • Y_I: Supply of imports (in real terms)
  • C_E: Total demand for exports
  • C_d_l [vector]: Demand for exports of specific product
  • C_l: Realised consumption by foreign consumers
  • Y_m [vector]: Supply of imports per sector
  • Q_m [vector]: Sales for imports per sector
  • Q_d_m [vector]: Demand for goods
  • P_m [vector]: Price of imports per sector
  • P_l: Price inflation of exports <- ??
source
BeforeIT.Workers — Type
This is a Workers. Each field is an array which stores the values for all the workers in the economy. Note that the O_h field is an integer, while the rest are floats.
For all fields the entry at index i corresponds to the ith worker.
Fields
  • Y_h: Net disposable income of worker owner (investor)
  • D_h: Deposits
  • K_h: Capital stock
  • w_h: Wages (0 if inactive or unemployed)
  • O_h: Occupation (0 if unemployed, -1 if inactive)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
source
Initialisation function
BeforeIT.init_model — Function
init_model(parameters, initial_conditions, T, typeInt = Int64, typeFloat = Float64)
Initializes the model with given parameters and initial conditions.
Parameters:
  • parameters: A dictionary containing the model parameters.
  • initial_conditions: A dictionary containing the initial conditions.
  • T (integer): The time horizon of the model.
  • typeInt: (optional, default: Int64): The data type to be used for integer values.
  • typeFloat: (optional, default: Float64): The data type to be used for floating-point values.
Returns:
  • model::Model: The initialized model.
source
BeforeIT.update_variables_with_totals! — Method
update_variables_with_totals!(model::Model)
Update the variables in the given model with some global quantities obtained from all agents. This is the last step in the initialization process and it must be performed after all agents have been initialized.
Arguments
  • model::Model: The model object to update.
Returns
  • Nothing
source
Functions to run an entire simulation
BeforeIT.run_one_epoch! — Method
run_one_epoch!(model; multi_threading = false)
This function simulates a single epoch the economic model, updating various components of the model based  the interactions between different economic agents. It accepts a model object, which encapsulates the state for the simulation, and an optional boolean parameter multi_threading to enable or disable multi-threading.
Key operations performed include:
  • Financial adjustments for firms and banks, including insolvency checks and profit calculations.
  • Economic expectations and adjustments, such as growth, inflation, and central bank rates.
  • Labor and credit market operations, including wage updates and loan processing.
  • Household economic activities, including consumption and investment budgeting.
  • Government and international trade financial activities, including budgeting and trade balances.
  • General market matching and accounting updates to reflect changes in economic indicators and positions.
The function updates the model in-place and does not return any value.
source
BeforeIT.run_n_sims — Method
run_n_sims(model, n_sims; shock = NoShock(), multi_threading = true)
A function that runs n_sims simulations in parallel with multiple threading and returns a vector of  data objects of dimension n_sims.
Arguments
  • model: The model configuration used to simulate.
  • n_sims: The number of simulations to run in parallel.
Returns
  • data_vector: A vector containing the data objects collected during each simulation.
source
BeforeIT.run_one_sim! — Method
run_one_sim!(model; shock = NoShock())
Run a single simulation based on the provided model.  The simulation runs for a number of epochs specified by model.prop.T.
Arguments
  • model::Model: The model configuration used for the simulation.
Returns
  • data::Data: The data collected during the simulation.
Details
The function initializes the data using BeforeIT.init_data(model), then iteratively updates the model and data for each epoch using BeforeIT.run_one_epoch!(model) and BeforeIT.update_data!(data, model) respectively.
Example
```julia model = BeforeIT.initializemodel(parameters, initialconditions, T) data = runonesim!(model)
source
Firms actions
BeforeIT.firms_deposits — Method
firms_deposits(firms, model)
Calculate the new deposits of firms.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • DD_i: Vector of new deposits
The new deposits DD_i are calculated as follows:
DD_i = sales + labour_cost + material_cost + taxes_products + taxes_production + corporate_tax + dividend_payments + interest_payments + interest_received + investment_cost + new_credit + debt_installment
where:
  • sales = P_i * Q_i
  • labour_cost = (1 + tau_SIF) * w_i * N_i * P_bar_HH
  • material_cost = -DM_i * P_bar_i
  • taxes_products = -tau_Y_i * P_i * Y_i
  • taxes_production = -tau_K_i * P_i * Y_i
  • corporate_tax = -tau_FIRM * pos(Pi_i)
  • dividend_payments = -theta_DIV * (1 - tau_FIRM) * pos(Pi_i)
  • interest_payments = -r * (L_i + pos(-D_i))
  • interest_received = r_bar * pos(D_i)
  • investment_cost = -P_CF_i * I_i
  • new_credit = DL_i
  • debt_installment = -theta * L_i
source
BeforeIT.firms_equity — Method
firms_equity(firms, model)
Calculate the equity of firms.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • E_i: Vector of equity
The equity E_i is calculated as follows:
\[E_i = D_i + M_i * \sum(a_{sg}[:, G_i] * \bar{P}_g) + P_i * S_i + \bar{P}_{CF} * K_i - L_i\]
where:
  • D_i: Deposits
  • M_i: Intermediate goods
  • a_sg: Technology coefficient of the gth product in the sth industry
  • G_i: Vector of goods
  • P_bar_g: Producer price index for principal good g
  • P_i: Price
  • S_i: Stock
  • P_bar_CF: Capital price index
  • K_i: Capital stock
  • L_i: Loans
source
BeforeIT.firms_expectations_and_decisions — Method
firms_expectations_and_decisions(firms, model)
Calculate the expectations and decisions of firms. That is: compute firm quantity, price, investment and intermediate-goods,  employment decisions, expected profits, and desired/expected loans and capital.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • Q_s_i: Vector of desired quantities
  • I_d_i: Vector of desired investments
  • DM_d_i: Vector of desired intermediate goods
  • N_d_i: Vector of desired employment
  • Pi_e_i: Vector of expected profits
  • DL_d_i: Vector of desired new loans
  • K_e_i: Vector of expected capital
  • L_e_i: Vector of expected loans
  • P_i: Vector of  prices
source
BeforeIT.firms_loans — Method
firms_loans(firms, model)
Calculate the new loans of firms.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • L_i: Vector of new loans
The new loans L_i are calculated as follows:
\[L_i = (1 - theta) * L_i + DL_i\]
where:
  • theta: Rate of repayment
  • L_i: Loans
  • DL_i: Acquired new loans
source
BeforeIT.firms_production — Method
firms_production(firms)
Calculate the production of firms.
Arguments
  • firms: Firms object
Returns
  • Y_i: Vector of production
The production Y_i is computed using a Leontief technology.
source
BeforeIT.firms_profits — Method
firms_profits(firms, model)
Calculate the profits of firms.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • Pi_i: Vector of profits
The profits Pi_i are calculated as follows:
Pi_i = in_sales + in_deposits - out_wages - out_expenses - out_depreciation - out_taxes_prods - out_taxes_capital - out_loans
where:
  • in_sales = P_i * Q_i + P_i * DS_i
  • in_deposits = r_bar * pos(D_i)
  • out_wages = (1 + tau_SIF) * w_i * N_i * P_bar_HH
  • out_expenses = 1 / beta_i * P_bar_i * Y_i
  • out_depreciation = delta_i / kappa_i * P_CF_i * Y_i
  • out_taxes_prods = tau_Y_i * P_i * Y_i
  • out_taxes_capital = tau_K_i * P_i * Y_i
  • out_loans = r * (L_i + pos(-D_i))
source
BeforeIT.firms_stocks — Method
firms_stocks(firms)
Calculate the stocks of firms.
Arguments
  • firms: Firms object
Returns
  • K_i: Vector of capital stock
  • M_i: Vector of intermediate goods
  • DS_i: Vector of differneces in stock of final goods
  • S_i: Vector of stock of final goods
The stocks are calculated as follows:
K_i = K_i - delta_i / kappa_i * Y_i + I_i
 M_i = M_i - Y_i / beta_i + DM_i
 DS_i = Y_i - Q_i
-S_i = S_i + DS_i
source
BeforeIT.firms_wages — Method
firms_wages(firms)
Calculate the wages set by firms.
Arguments
  • firms: Firms object
Returns
  • w_i: Vector of wages
source
BeforeIT.init_firms — Method
init_firms(parameters, initial_conditions; typeInt = Int64, typeFloat = Float64)
Initialize firms with given parameters and initial conditions.
Arguments
  • parameters: The parameters for initializing the firms.
  • initial_conditions: The initial conditions for the firms.
  • typeInt: (optional) The integer type to be used. Default is Int64.
  • typeFloat: (optional) The floating-point type to be used. Default is Float64.
Returns
  • firms::Firms: The initialized firms.
  • firms_args::Tuple: The arguments used to initialize the firms.
source
BeforeIT.leontief_production — Method
leontief_production(Q_s_i, N_i, alpha_i, K_i, kappa_i, M_i, beta_i)
Calculate the production function of firms.
Arguments
  • Q_s_i: Vector of desired quantities
  • N_i: Vector of employment
  • alpha_i: Vector of labour productivity
  • K_i: Vector of capital stock
  • kappa_i: Vector of capital productivity
  • M_i: Vector of intermediate goods
  • beta_i: Vector of intermediate goods productivity
Returns
  • Y_i: Vector of production
The Leontief production function Y_i is calculated as follows:
\[Y_i = \min(Q_s_i, \min(N_i \cdot \alpha_i, \min(K_i \cdot \kappa_i, M_i \cdot \beta_i)))\]
source
Households actions
Government actions
BeforeIT.gov_expenditure — Method
gov_expenditure(gov::AbstractGovernment, model)
Computes government expenditure on consumption and transfers to households.
Arguments
  • gov: government object
  • model: model object
Returns
  • C_G: government consumption
  • C_d_j: local government consumptions
source
BeforeIT.gov_loans — Method
gov_loans(gov::AbstractGovernment, model, Y_G)
Computes government new government debt.
Arguments
  • gov::AbstractGovernment: government object
  • model: model object
Returns
  • L_G: new government debt
source
BeforeIT.gov_revenues — Method
gov_revenues(model)
Computes government revenues from taxes and social security contributions. The government collects taxes on labour income, capital income, value added, and corporate income. It also collects social security contributions from workers and firms. The government also collects taxes on consumption and capital formation. Finally, the government collects taxes on exports and imports.
Arguments
  • model: model object
Returns
  • Y_G: government revenues
source
BeforeIT.gov_social_benefits — Method
gov_social_benefits(gov::AbstractGovernment, model)
Computes social benefits paid by the government households.
Arguments
  • gov: government object
  • model: model object
Returns
  • sb_other: social benefits for other households
  • sb_inact: social benefits for inactive households
source
BeforeIT.init_government — Method
init_government(parameters, initial_conditions; typeInt = Int64, typeFloat = Float64)
Initialize the government agent.
Arguments
  • parameters: The parameters.
  • initial_conditions: The initial conditions.
  • typeInt: The integer type to be used (default: Int64).
  • typeFloat: The floating-point type to be used (default: Float64).
Returns
  • The initialized government model.
  • The arguments used to initialize the government model.
source
Bank and Central Bank actions
BeforeIT._bank_deposits — Method
_deposit_bank(waD_h, wiD_h, fD_h, bD_h, fD_i, bE_k, fL_i)
Helper function to calculate the new deposits of a bank.
Arguments
  • waD_h: Array of deposits from active workers
  • wiD_h: Array of deposits from inactive workers
  • fD_h: Array of deposits from firms
  • bD_h: Deposits from the bank owner
  • fD_i: Array of deposits from firms
  • bE_k: Bank equity
  • fL_i: Array of loans to firms
Returns
  • D_k: New deposits of the bank
The new deposits D_k are calculated as the sum of the deposits of the active workers, the inactive workers, the firms, and the bank owner itself, plus the bank's equity, minus the loans of the firms.
source
BeforeIT._bank_profits — Method
_bank_profits(L_i, D_i, D_h, D_k, r_bar, r)
Helper function to calculate the total profits of a bank.
Arguments
  • L_i: Array of loans provided by the bank
  • D_i: Array of deposits from firms
  • D_h: Array of deposits from households
  • D_k: Residual and balancing item on the bank’s balance sheet
  • r_bar: Base interest rate
  • r: Interest rate set by the bank
Returns
  • Pi_k: Total profits of the bank
The total profits Pi_k are calculated as follows:
\[\Pi_k = r \cdot \sum_i(L_i + \max(0, -D_i)) + r \cdot \sum_h(\max(0, -D_h)) + r_{bar} 
+S_i = S_i + DS_i
source
BeforeIT.firms_wages — Method
firms_wages(firms)

Calculate the wages set by firms.

Arguments

  • firms: Firms object

Returns

  • w_i: Vector of wages
source
BeforeIT.init_firms — Method
init_firms(parameters, initial_conditions; typeInt = Int64, typeFloat = Float64)

Initialize firms with given parameters and initial conditions.

Arguments

  • parameters: The parameters for initializing the firms.
  • initial_conditions: The initial conditions for the firms.
  • typeInt: (optional) The integer type to be used. Default is Int64.
  • typeFloat: (optional) The floating-point type to be used. Default is Float64.

Returns

  • firms::Firms: The initialized firms.
  • firms_args::Tuple: The arguments used to initialize the firms.
source
BeforeIT.leontief_production — Method
leontief_production(Q_s_i, N_i, alpha_i, K_i, kappa_i, M_i, beta_i)

Calculate the production function of firms.

Arguments

  • Q_s_i: Vector of desired quantities
  • N_i: Vector of employment
  • alpha_i: Vector of labour productivity
  • K_i: Vector of capital stock
  • kappa_i: Vector of capital productivity
  • M_i: Vector of intermediate goods
  • beta_i: Vector of intermediate goods productivity

Returns

  • Y_i: Vector of production

The Leontief production function Y_i is calculated as follows:

\[Y_i = \min(Q_s_i, \min(N_i \cdot \alpha_i, \min(K_i \cdot \kappa_i, M_i \cdot \beta_i)))\]

source

Households actions

Government actions

BeforeIT.gov_expenditure — Method
gov_expenditure(gov::AbstractGovernment, model)

Computes government expenditure on consumption and transfers to households.

Arguments

  • gov: government object
  • model: model object

Returns

  • C_G: government consumption
  • C_d_j: local government consumptions
source
BeforeIT.gov_loans — Method
gov_loans(gov::AbstractGovernment, model, Y_G)

Computes government new government debt.

Arguments

  • gov::AbstractGovernment: government object
  • model: model object

Returns

  • L_G: new government debt
source
BeforeIT.gov_revenues — Method
gov_revenues(model)

Computes government revenues from taxes and social security contributions. The government collects taxes on labour income, capital income, value added, and corporate income. It also collects social security contributions from workers and firms. The government also collects taxes on consumption and capital formation. Finally, the government collects taxes on exports and imports.

Arguments

  • model: model object

Returns

  • Y_G: government revenues
source
BeforeIT.gov_social_benefits — Method
gov_social_benefits(gov::AbstractGovernment, model)

Computes social benefits paid by the government households.

Arguments

  • gov: government object
  • model: model object

Returns

  • sb_other: social benefits for other households
  • sb_inact: social benefits for inactive households
source
BeforeIT.init_government — Method
init_government(parameters, initial_conditions; typeInt = Int64, typeFloat = Float64)

Initialize the government agent.

Arguments

  • parameters: The parameters.
  • initial_conditions: The initial conditions.
  • typeInt: The integer type to be used (default: Int64).
  • typeFloat: The floating-point type to be used (default: Float64).

Returns

  • The initialized government model.
  • The arguments used to initialize the government model.
source

Bank and Central Bank actions

BeforeIT._bank_deposits — Method
_deposit_bank(waD_h, wiD_h, fD_h, bD_h, fD_i, bE_k, fL_i)

Helper function to calculate the new deposits of a bank.

Arguments

  • waD_h: Array of deposits from active workers
  • wiD_h: Array of deposits from inactive workers
  • fD_h: Array of deposits from firms
  • bD_h: Deposits from the bank owner
  • fD_i: Array of deposits from firms
  • bE_k: Bank equity
  • fL_i: Array of loans to firms

Returns

  • D_k: New deposits of the bank

The new deposits D_k are calculated as the sum of the deposits of the active workers, the inactive workers, the firms, and the bank owner itself, plus the bank's equity, minus the loans of the firms.

source
BeforeIT._bank_profits — Method
_bank_profits(L_i, D_i, D_h, D_k, r_bar, r)

Helper function to calculate the total profits of a bank.

Arguments

  • L_i: Array of loans provided by the bank
  • D_i: Array of deposits from firms
  • D_h: Array of deposits from households
  • D_k: Residual and balancing item on the bank’s balance sheet
  • r_bar: Base interest rate
  • r: Interest rate set by the bank

Returns

  • Pi_k: Total profits of the bank

The total profits Pi_k are calculated as follows:

\[\Pi_k = r \cdot \sum_i(L_i + \max(0, -D_i)) + r \cdot \sum_h(\max(0, -D_h)) + r_{bar} \cdot \max(0, D_k) - r_{bar} \cdot \sum_i(\max(0, D_i)) - r_{bar} \cdot -\sum_h(\max(0, D_h)) - r_{bar} \cdot \max(0, -D_k)\]

source
BeforeIT._central_bank_profits — Method
_central_bank_profits(r_bar, D_k, L_G, r_G)

Helper function to calculate the profits of a central bank.

Arguments

  • r_bar: The base interest rate
  • D_k: Deposits from commercial banks
  • L_G: Loans provided to the government
  • r_G: Interest rate on government loans

Returns

  • Pi_CB: Profits of the central bank

The profits Pi_CB are calculated as follows:

\[\{Pi}_{CB} = r_{G} \cdot L_{G} - r_{bar} \cdot D_{k}\]

source
BeforeIT.bank_deposits — Method
deposits_bank(bank, w_act, w_inact, firms)

Calculate the new deposits of a bank.

Arguments

  • bank: The Bank object containing the bank of the model
  • w_act: The Workers object containing the active workers of the model
  • w_inact: The Workers object containing the inactive workers of the model
  • firms: The Firms object containing the firms of the model

Returns

  • D_k: New deposits of the bank

The new deposits D_k are calculated as the sum of the deposits of the active workers, the inactive workers, the firms, and the bank owner itself, plus the bank's equity, minus the loans of the firms.

source
BeforeIT.bank_equity — Method
bank_equity(bank, model)

Calculate the net profits of a bank.

Arguments

  • bank: The bank object.
  • model: The model object.

Returns

  • E_k: The updated equity of the bank.

The net profits DE_k are calculated as:

\[DE_k = \Pi_k - \theta_{DIV} \cdot (1 - \tau_{FIRM}) \cdot \max(0, \Pi_k) - \tau_{FIRM} \cdot \max(0, \Pi_k)\]

and the equity E_k is updated as:

\[E_k = E_k + DE_k\]

source
BeforeIT.bank_expected_profits — Method
bank_expected_profits(Pi_k, pi_e, gamma_e)

Calculate the expected profits of a bank.

Arguments

  • Pi_k: Past profits of the bank
  • pi_e: Expected inflation rate
  • gamma_e: Expected growth rate

Returns

  • E_Pi_k: Expected profits of the bank

The expected profits E_Pi_k are calculated as follows:

\[E_{\Pi_k} = \Pi_k \cdot (1 + \pi_e) \cdot (1 + \gamma_e)\]

source
BeforeIT.bank_profits — Method
bank_profits(bank, model)

Calculate the total profits of a bank.

Arguments

  • bank: The bank object.
  • model: The model object.

Returns

  • Pi_k: The total profits of the bank.

The total profits Pi_k are calculated as:

\[\Pi_k = r \cdot \sum_i(L_i + \max(0, -D_i)) + r \cdot \sum_h(\max(0, -D_h)) + r_{bar} +\sum_h(\max(0, D_h)) - r_{bar} \cdot \max(0, -D_k)\]

source
BeforeIT._central_bank_profits — Method
_central_bank_profits(r_bar, D_k, L_G, r_G)

Helper function to calculate the profits of a central bank.

Arguments

  • r_bar: The base interest rate
  • D_k: Deposits from commercial banks
  • L_G: Loans provided to the government
  • r_G: Interest rate on government loans

Returns

  • Pi_CB: Profits of the central bank

The profits Pi_CB are calculated as follows:

\[\{Pi}_{CB} = r_{G} \cdot L_{G} - r_{bar} \cdot D_{k}\]

source
BeforeIT.bank_deposits — Method
deposits_bank(bank, w_act, w_inact, firms)

Calculate the new deposits of a bank.

Arguments

  • bank: The Bank object containing the bank of the model
  • w_act: The Workers object containing the active workers of the model
  • w_inact: The Workers object containing the inactive workers of the model
  • firms: The Firms object containing the firms of the model

Returns

  • D_k: New deposits of the bank

The new deposits D_k are calculated as the sum of the deposits of the active workers, the inactive workers, the firms, and the bank owner itself, plus the bank's equity, minus the loans of the firms.

source
BeforeIT.bank_equity — Method
bank_equity(bank, model)

Calculate the net profits of a bank.

Arguments

  • bank: The bank object.
  • model: The model object.

Returns

  • E_k: The updated equity of the bank.

The net profits DE_k are calculated as:

\[DE_k = \Pi_k - \theta_{DIV} \cdot (1 - \tau_{FIRM}) \cdot \max(0, \Pi_k) - \tau_{FIRM} \cdot \max(0, \Pi_k)\]

and the equity E_k is updated as:

\[E_k = E_k + DE_k\]

source
BeforeIT.bank_expected_profits — Method
bank_expected_profits(Pi_k, pi_e, gamma_e)

Calculate the expected profits of a bank.

Arguments

  • Pi_k: Past profits of the bank
  • pi_e: Expected inflation rate
  • gamma_e: Expected growth rate

Returns

  • E_Pi_k: Expected profits of the bank

The expected profits E_Pi_k are calculated as follows:

\[E_{\Pi_k} = \Pi_k \cdot (1 + \pi_e) \cdot (1 + \gamma_e)\]

source
BeforeIT.bank_profits — Method
bank_profits(bank, model)

Calculate the total profits of a bank.

Arguments

  • bank: The bank object.
  • model: The model object.

Returns

  • Pi_k: The total profits of the bank.

The total profits Pi_k are calculated as:

\[\Pi_k = r \cdot \sum_i(L_i + \max(0, -D_i)) + r \cdot \sum_h(\max(0, -D_h)) + r_{bar} \cdot \max(0, D_k) - r_{bar} \cdot \sum_i(\max(0, D_i)) - r_{bar} \cdot -\sum_h(\max(0, D_h)) - r_{bar} \cdot \max(0, -D_k)\]

source
BeforeIT.bank_rate — Method
bank_rate(bank, model)

Update the interest rate set by the bank.

Arguments

  • bank: The bank whose interest rate is to be updated
  • model: Model object

Returns

  • r: The updated interest rate

\[r = \bar{r} + \mu\]

source
BeforeIT.central_bank_equity — Method
central_bank_equity(cb, model)

Calculate the equity of the central bank.

Arguments

  • cb: The central bank
  • model: The model object

Returns

  • E_CB: The equity of the central bank

The equity E_CB is calculated as follows:

\[E_{CB} = E_{CB} + \Pi_{CB}\]

where \Pi_{CB} are the profits of the central bank.

source
BeforeIT.central_bank_rate — Method
central_bank_rate(cb, model)

Update the base interest rate set by the central bank according to the Taylor rule.

Arguments

  • cb: The central bank whose base interest rate is to be updated
  • model: The model object

Returns

  • r_bar: The updated base interest rate
source
BeforeIT.finance_insolvent_firms! — Method
finance_insolvent_firms!(firms, bank, P_bar_CF, zeta_b,  insolvent)

Rifinance insolvent firms using bank equity.

Arguments

  • firms: The Firms object containing the firms of the model
  • bank: The Bank object containing the bank of the model
  • P_bar_CF: Capital price index
  • zeta_b: Parameter of loan-to-capital ratio for new firms after bankruptcy

Returns

  • This function does not return a value. It modifies the banks and firms collections in-place.
source
BeforeIT.taylor_rule — Method
taylor_rule(rho, r_bar, r_star, pi_star, xi_pi, xi_gamma, gamma_EA, pi_EA)

Calculate the interest rate according to the Taylor rule.

Arguments

  • rho: Parameter for gradual adjustment of the policy rate.
  • r_bar: Nominal interest rate.
  • r_star: Real equilibrium interest rate.
  • pi_star: The target inflation rate.
  • xi_pi: Weight the CB puts on inflation targeting.
  • xi_gamma: Weight placed on economic growth.
  • gamma_EA: The output growth rate.
  • pi_EA: The inflation rate.

Returns

  • rate: The calculated interest rate.

The Taylor rule is given by the following equation:

\[r_t = ρ * r_{t-1} + (1 - ρ) * (r^* + π^* + ξ_π * (π_t - π^*) + ξ_γ * γ_t)```\]

source

Rest Of The World actions

BeforeIT.rotw_deposits — Method
rotw_deposits(rotw, tau_EXPORT)

Calculate the deposits of the rest of the world.

Arguments

  • rotw: The rest of the world object.
  • tau_EXPORT: The export tax.

Returns

  • D_RoW: The deposits of the rest of the world.

The deposits D_RoW are calculated as follows:

\[D_{RoW} = D_{RoW} + \left( \sum_{m} P_m \cdot Q_m \right) - (1 + \tau_{EXPORT}) \cdot C_l\]

source
BeforeIT.rotw_import_export — Method
rotw_import_export(rotw, model, pi_e, epsilon_E, epsilon_I)

Calculate the demand for exports and supply of imports of the rest of the world.

Arguments

  • rotw: The rest of the world object.
  • model: The model object.

Returns

  • C_E: Total demand for exports.
  • Y_I: Supply of imports (in real terms).
  • C_d_l: TDemand for exports of specific product.
  • Y_m: Supply of imports per sector.
  • P_m: Price of imports per sector.
source

Markets

BeforeIT.search_and_matching_credit — Method
search_and_matching_credit(firms::Firms, model)

This function calculates the credit allocation for each firm in the given firms object.

Parameters:

  • firms::Firms: The firms object.
  • model: The model object.

Returns:

  • DL_i: An array of credit allocations for each firm.
source
BeforeIT.search_and_matching_labour — Method
search_and_matching_labour(firms::Firms, model)

This function implements a labor search and matching algorithm. It takes in a Firms object and a Model object as input. The Firms object contains information about the number of desired employees (N_d_i) and the current number of employees (N_i) for each firm. The model object contains information about the current employment status (O_h) of each worker.

The function performs the following steps:

  • Calculates the vacancies (V_i) for each firm as the difference between desired and current employees.
  • Identifies employed workers and shuffles them randomly.
  • Fires workers from firms with negative vacancies to adjust the workforce.
  • Identifies unemployed workers and firms with positive vacancies.
  • Randomly matches unemployed workers to firms with vacancies until all vacancies are filled or there are no more unemployed workers.

The function returns:

  • N_i: An updated array of the number of employed workers for each firm.
  • O_h: An updated array where each element represents the firm a worker is employed with (0 if unemployed).
source
BeforeIT.search_and_matching! — Function
search_and_matching!(model, multi_threading::Bool = false)

This function performs a search and matching algorithm for firms and for retail markets. It takes in a model object and an optional boolean argument for multi-threading. The function loops over all goods and performs the firms market and retail market operations for each good. Finally, it updates the aggregate variables based on the results of markets.

Args:

  • model: The model object
  • multi_threading: A boolean indicating whether to use multi-threading for the algorithm. Default is false.

This function updates the model in-place and does not return any value.

source
+\sum_h(\max(0, D_h)) - r_{bar} \cdot \max(0, -D_k)\]

source
BeforeIT.bank_rate — Method
bank_rate(bank, model)

Update the interest rate set by the bank.

Arguments

  • bank: The bank whose interest rate is to be updated
  • model: Model object

Returns

  • r: The updated interest rate

\[r = \bar{r} + \mu\]

source
BeforeIT.central_bank_equity — Method
central_bank_equity(cb, model)

Calculate the equity of the central bank.

Arguments

  • cb: The central bank
  • model: The model object

Returns

  • E_CB: The equity of the central bank

The equity E_CB is calculated as follows:

\[E_{CB} = E_{CB} + \Pi_{CB}\]

where \Pi_{CB} are the profits of the central bank.

source
BeforeIT.central_bank_rate — Method
central_bank_rate(cb, model)

Update the base interest rate set by the central bank according to the Taylor rule.

Arguments

  • cb: The central bank whose base interest rate is to be updated
  • model: The model object

Returns

  • r_bar: The updated base interest rate
source
BeforeIT.finance_insolvent_firms! — Method
finance_insolvent_firms!(firms, bank, P_bar_CF, zeta_b,  insolvent)

Rifinance insolvent firms using bank equity.

Arguments

  • firms: The Firms object containing the firms of the model
  • bank: The Bank object containing the bank of the model
  • P_bar_CF: Capital price index
  • zeta_b: Parameter of loan-to-capital ratio for new firms after bankruptcy

Returns

  • This function does not return a value. It modifies the banks and firms collections in-place.
source
BeforeIT.taylor_rule — Method
taylor_rule(rho, r_bar, r_star, pi_star, xi_pi, xi_gamma, gamma_EA, pi_EA)

Calculate the interest rate according to the Taylor rule.

Arguments

  • rho: Parameter for gradual adjustment of the policy rate.
  • r_bar: Nominal interest rate.
  • r_star: Real equilibrium interest rate.
  • pi_star: The target inflation rate.
  • xi_pi: Weight the CB puts on inflation targeting.
  • xi_gamma: Weight placed on economic growth.
  • gamma_EA: The output growth rate.
  • pi_EA: The inflation rate.

Returns

  • rate: The calculated interest rate.

The Taylor rule is given by the following equation:

\[r_t = ρ * r_{t-1} + (1 - ρ) * (r^* + π^* + ξ_π * (π_t - π^*) + ξ_γ * γ_t)```\]

source

Rest Of The World actions

BeforeIT.rotw_deposits — Method
rotw_deposits(rotw, tau_EXPORT)

Calculate the deposits of the rest of the world.

Arguments

  • rotw: The rest of the world object.
  • tau_EXPORT: The export tax.

Returns

  • D_RoW: The deposits of the rest of the world.

The deposits D_RoW are calculated as follows:

\[D_{RoW} = D_{RoW} + \left( \sum_{m} P_m \cdot Q_m \right) - (1 + \tau_{EXPORT}) \cdot C_l\]

source
BeforeIT.rotw_import_export — Method
rotw_import_export(rotw, model, pi_e, epsilon_E, epsilon_I)

Calculate the demand for exports and supply of imports of the rest of the world.

Arguments

  • rotw: The rest of the world object.
  • model: The model object.

Returns

  • C_E: Total demand for exports.
  • Y_I: Supply of imports (in real terms).
  • C_d_l: TDemand for exports of specific product.
  • Y_m: Supply of imports per sector.
  • P_m: Price of imports per sector.
source

Markets

BeforeIT.search_and_matching_credit — Method
search_and_matching_credit(firms::Firms, model)

This function calculates the credit allocation for each firm in the given firms object.

Parameters:

  • firms::Firms: The firms object.
  • model: The model object.

Returns:

  • DL_i: An array of credit allocations for each firm.
source
BeforeIT.search_and_matching_labour — Method
search_and_matching_labour(firms::Firms, model)

This function implements a labor search and matching algorithm. It takes in a Firms object and a Model object as input. The Firms object contains information about the number of desired employees (N_d_i) and the current number of employees (N_i) for each firm. The model object contains information about the current employment status (O_h) of each worker.

The function performs the following steps:

  • Calculates the vacancies (V_i) for each firm as the difference between desired and current employees.
  • Identifies employed workers and shuffles them randomly.
  • Fires workers from firms with negative vacancies to adjust the workforce.
  • Identifies unemployed workers and firms with positive vacancies.
  • Randomly matches unemployed workers to firms with vacancies until all vacancies are filled or there are no more unemployed workers.

The function returns:

  • N_i: An updated array of the number of employed workers for each firm.
  • O_h: An updated array where each element represents the firm a worker is employed with (0 if unemployed).
source
BeforeIT.perform_firms_market! — Method

Perform the firms market exchange process

source
BeforeIT.perform_retail_market! — Method

Perform the retail market exchange process

source
BeforeIT.search_and_matching! — Function
search_and_matching!(model, multi_threading::Bool = false)

This function performs a search and matching algorithm for firms and for retail markets. It takes in a model object and an optional boolean argument for multi-threading. The function loops over all goods and performs the firms market and retail market operations for each good. Finally, it updates the aggregate variables based on the results of markets.

Args:

  • model: The model object
  • multi_threading: A boolean indicating whether to use multi-threading for the algorithm. Default is false.

This function updates the model in-place and does not return any value.

source
diff --git a/dev/examples/basic_example-3aeaf5c3.svg b/dev/examples/basic_example-3aeaf5c3.svg new file mode 100644 index 0000000..bb51370 --- /dev/null +++ b/dev/examples/basic_example-3aeaf5c3.svg @@ -0,0 +1,44 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/basic_example-407f8365.svg b/dev/examples/basic_example-407f8365.svg deleted file mode 100644 index 1947081..0000000 --- a/dev/examples/basic_example-407f8365.svg +++ /dev/null @@ -1,46 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/basic_example-4262beec.svg b/dev/examples/basic_example-4262beec.svg deleted file mode 100644 index 4cba99b..0000000 --- a/dev/examples/basic_example-4262beec.svg +++ /dev/null @@ -1,282 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/basic_example-6cf98736.svg b/dev/examples/basic_example-6cf98736.svg new file mode 100644 index 0000000..8878c5f --- /dev/null +++ b/dev/examples/basic_example-6cf98736.svg @@ -0,0 +1,275 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/basic_example-ecde5600.svg b/dev/examples/basic_example-ecde5600.svg deleted file mode 100644 index d61d287..0000000 --- a/dev/examples/basic_example-ecde5600.svg +++ /dev/null @@ -1,275 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/basic_example-f011c18e.svg b/dev/examples/basic_example-f011c18e.svg new file mode 100644 index 0000000..d8e1601 --- /dev/null +++ b/dev/examples/basic_example-f011c18e.svg @@ -0,0 +1,280 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/basic_example.html b/dev/examples/basic_example.html index 90ba045..ce2151a 100644 --- a/dev/examples/basic_example.html +++ b/dev/examples/basic_example.html @@ -22,10 +22,10 @@ "Y_EA" => 2.35485e6 "D_I" => 54049.0 ⋮ => ⋮

We can now initialise the model, by specifying in advance the maximum number of epochs.

T = 16
-model = Bit.init_model(parameters, initial_conditions, T)
Model(Workers{Vector{Float64}, Vector{Int64}}([0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249  …  4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569], [3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487  …  22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958], [6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249  …  42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032], [0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983  …  11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522], [1, 2, 3, 4, 5, 5, 5, 5, 5, 5  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), Workers{Vector{Float64}, Vector{Int64}}([2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714  …  2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714], [13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984  …  13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984], [25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977  …  25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1  …  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), Firms{Vector{Float64}, Vector{Int64}}([1, 1, 1, 1, 1, 1, 1, 1, 1, 1  …  62, 62, 62, 62, 62, 62, 62, 62, 62, 62], [10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621  …  11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951], [1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485  …  3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877], [0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754  …  0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983  …  2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687], [0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721  …  0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782], [0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832  …  0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318], [-0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635  …  0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037], [1, 1, 1, 1, 10, 1, 4, 2, 3, 1  …  1, 2, 1, 2, 1, 2, 6, 2, 26, 1], [10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 108.22026659306621, 10.822026659306621, 43.288106637226484, 21.644053318613242, 32.46607997791986, 10.822026659306621  …  11.988103388298951, 23.976206776597902, 11.988103388298951, 23.976206776597902, 11.988103388298951, 23.976206776597902, 71.9286203297937, 23.976206776597902, 311.6906880957727, 11.988103388298951], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 108.22026659306621, 10.822026659306621, 43.288106637226484, 21.644053318613242, 32.46607997791986, 10.822026659306621  …  11.988103388298951, 23.976206776597902, 11.988103388298951, 23.976206776597902, 11.988103388298951, 23.976206776597902, 71.9286203297937, 23.976206776597902, 311.6906880957727, 11.988103388298951], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0  …  1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [298.15365853658534, 298.15365853658534, 298.15365853658534, 298.15365853658534, 2981.5365853658536, 298.15365853658534, 1192.6146341463414, 596.3073170731707, 894.460975609756, 298.15365853658534  …  79.20833333333334, 158.41666666666669, 79.20833333333334, 158.41666666666669, 79.20833333333334, 158.41666666666669, 475.25, 158.41666666666669, 2059.4166666666665, 79.20833333333334], [7.65511425407355, 7.65511425407355, 7.65511425407355, 7.65511425407355, 76.5511425407355, 7.65511425407355, 30.6204570162942, 15.3102285081471, 22.965342762220647, 7.65511425407355  …  4.057486672347064, 8.114973344694128, 4.057486672347064, 8.114973344694128, 4.057486672347064, 8.114973344694128, 24.344920034082385, 8.114973344694128, 105.49465348102368, 4.057486672347064], [94.08074124608002, 94.08074124608002, 94.08074124608002, 94.08074124608002, 940.8074124608002, 94.08074124608002, 376.3229649843201, 188.16148249216005, 282.24222373824006, 94.08074124608002  …  24.993752380711385, 49.98750476142277, 24.993752380711385, 49.98750476142277, 24.993752380711385, 49.98750476142277, 149.96251428426828, 49.98750476142277, 649.8375618984958, 24.993752380711385], [0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726  …  0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956], [12.384292053067009, 12.384292053067009, 12.384292053067009, 12.384292053067009, 123.84292053067009, 12.384292053067009, 49.537168212268035, 24.768584106134018, 37.15287615920102, 12.384292053067009  …  13.5301114965952, 27.0602229931904, 13.5301114965952, 27.0602229931904, 13.5301114965952, 27.0602229931904, 81.1806689795712, 27.0602229931904, 351.7828989114752, 13.5301114965952], [1.254845467476436, 1.254845467476436, 1.254845467476436, 1.254845467476436, 12.548454674764363, 1.254845467476436, 5.019381869905744, 2.509690934952872, 3.764536402429307, 1.254845467476436  …  3.5771064837982873, 7.154212967596575, 3.5771064837982873, 7.154212967596575, 3.5771064837982873, 7.154212967596575, 21.462638902789724, 7.154212967596575, 93.00476857875546, 3.5771064837982873], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.3061857958470842, 1.3061857958470842, 1.3061857958470842, 1.3061857958470842, 7.749284819252173, 1.3061857958470842, 3.4538854703154467, 2.0220856873365385, 2.7379855788259917, 1.3061857958470842  …  2.631055234104207, 4.671824563850785, 2.631055234104207, 4.671824563850785, 2.631055234104207, 4.671824563850785, 12.834901882837093, 4.671824563850785, 53.650288477768626, 2.631055234104207], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [11.889212387273576, 11.889212387273576, 11.889212387273576, 11.889212387273576, 70.53582526964644, 11.889212387273576, 31.438083348064524, 18.405502707537227, 24.921793027800867, 11.889212387273576  …  23.948487711601803, 42.52405335619368, 23.948487711601803, 42.52405335619368, 23.948487711601803, 42.52405335619368, 116.8263159345612, 42.52405335619368, 488.33762882639866, 23.948487711601803], [6.44766966354178, 6.44766966354178, 6.44766966354178, 6.44766966354178, 38.25246668743173, 6.44766966354178, 17.049268671505093, 9.981535999529552, 13.515402335517319, 6.44766966354178  …  12.98756660038165, 23.06132987320929, 12.98756660038165, 23.06132987320929, 12.98756660038165, 23.06132987320929, 63.356382964519845, 23.06132987320929, 264.8316484210726, 12.98756660038165]), Bank{Float64}(89460.0, 6476.292527744362, 0.0, 126431.0, 0.028359903595743693, 3695.3696336675234, 0.0, 0.0, 0.0, 0.0, 33636.12938055409, 18241.29672694816), CentralBank{Float64}(0.0016459319014481277, 0.0089810924595537, 0.9259668580654086, -0.003424572940686137, 0.0049629315732038215, 0.30996974466133875, 1.328593153520194, 106179.90000000002), Government{Float64}(0.9905949533296431, 0.09373211872949586, 0.011235005057648862, 0.0, 14732.121510837034, 232610.9, 2.238468336136841, 0.5902859043576301, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0, 0.0), RestOfTheWorld{Float64}(0.962809216044625, 0.39260026877953946, 0.020320381298662014, 0.9662360466537488, 0.35492769963078624, 0.02122821278168188, 2.3548476e6, 0.0, 0.0019383188997990075, 0.38456173629534834, 0.0026219533879005877, 0.0025327891562467505, 0.9635784504324201, 0.5360029623199525, 0.006618207536795881, 0.0, 33097.63671130043, 34095.03119997918, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0), Aggregates{Float64, Int64}([104531.39273728609, 105062.38754395355, 105399.12953350678, 106689.88106040593, 107938.33111423723, 108890.48532381697, 110110.17779727321, 110374.00540561741, 110808.89423399912, 111932.48072916963  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.007497362866886709, -0.007895434153021436, -0.0019938777296781562, -0.0035311300388783107, 0.001212170001002849, -0.001672412335241874, 0.001839696090252002, 0.004290005139261838, 0.00600429551344886, 0.0036060572293247772  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1.0, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0  …  1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1), MutableNamedTuple(tau_VAT = 0.1528683933530887, tau_EXPORT = 0.0029486201783457183, tau_SIW = 0.17114894621657745, psi_H = 0.07125099957246343, tau_FIRM = 0.07701197259426128, H_inact = 4130, theta_DIV = 0.7858074440019603, I_s = [48, 2, 1, 1, 5, 2, 4, 1, 1, 1  …  14, 10, 13, 41, 20, 16, 7, 7, 2, 19], psi = 0.9096681249468772, tau_INC = 0.21340742230566648, zeta_b = 0.5, tau_CF = 0.08761417854834112, H_act = 4743, zeta = 0.03, mu = 0.026713971694295565, tau_G = 0.009147800682711324, theta_UB = 0.3585824478060919, T_prime = 54, tau_SIF = 0.21215146534992413, T = 16, zeta_LTV = 0.6, I = 624, products = MutableNamedTuple(a_sg = [0.37790282216028437 0.0 … 0.0 0.0; 0.0006712800413285777 0.8149348034258406 … 2.4029219530949635e-5 0.00019143106686926454; … ; 0.00037430822580994426 0.000357977071568566 … 0.3197327950788158 0.0011366219595362582; 0.0 5.36965607352849e-5 … 0.0 0.05594572929254256], b_CF_g = [0.0033476048872100555, 0.0, 0.0, 0.0008050086095806136, 0.0, 0.003306696048303853, 0.0030629933432974495, 0.0, 0.0, 0.0  …  0.0017883064872300512, 0.0, 0.0, 0.0, 0.0, 0.002291709084994238, 0.0, 0.0, 0.0, 0.0], b_CFH_g = [0.0006092803753845975, 0.0, 0.0, 0.004372477702289994, 0.0, 0.0, 0.06710603871527036, 0.0, 0.0, 0.0  …  0.0041711096896454745, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], c_E_g = [0.005090338238241512, 0.0005933402816643566, 1.793599921320476e-5, 0.007268146142708006, 0.05434404438533037, 0.02138421321578873, 0.023894091259534518, 0.028037089231640524, 0.005923381894006229, 0.011927802553688313  …  0.0014618202435669027, 0.0009447261124040238, 0.0001280209174610526, 0.0010477673386531637, 0.0, 0.0013030104043795392, 5.5260305268213854e-5, 0.0, 1.307076865739618e-5, 8.641230390167474e-6], b_HH_g = [0.011287997598927976, 0.0020040637862256817, 0.0003326837475323491, 0.00034402684015257527, 0.06305103828047173, 0.03068200872784047, 0.0003505790613555631, 0.0020473225369636873, 0.0, 0.0191077565617325  …  0.005591228759882936, 0.0004356874830029748, 0.014434120586233577, 0.03514505772295519, 0.024907176866291014, 0.013342154173060372, 0.009686226103767459, 0.011051272187723254, 0.0021206651420422927, 0.01721782824162338], c_G_g = [0.0, 0.0, 0.0, 0.0, 8.57086390274792e-6, 0.0, 0.0, 0.0, 2.4536198623552867e-5, 0.0  …  0.008555738848801895, 0.3324338967920097, 0.22067672180252998, 0.2370889178393625, 0.0477595425645907, 0.012942508661614227, 0.004667927760053456, 0.021757222045203074, 0.0, 0.002000708524749294], c_I_g = [0.016810689305736877, 0.004087420487057966, 0.00036885674795364003, 0.05818437780960789, 0.04895082866561155, 0.04689140072807505, 0.010101572733480902, 0.016066325760592727, 0.00037371898454196314, 0.041008926225895866  …  0.0018123421762754539, 0.000401097116716159, 0.0003479865324437806, 0.0006664256271585853, 0.0028556289501379516, 0.001307065586721844, 6.418152296577174e-5, 0.0, 3.538212163497461e-5, 0.00020458795490837936]), J = 156, G = 62, H = 8873, L = 312, theta = 0.05, C = [4.3800671000101816e-5 0.00010629745355671226 9.959785873214212e-5; 0.00010629745355671226 0.0004129178961230129 0.0003596689472264872; 9.959785873214212e-5 0.0003596689472264872 0.0004506370179043619]))

Note that the it is very simple to inspect the model by typing

fieldnames(typeof(model))
(:w_act, :w_inact, :firms, :bank, :cb, :gov, :rotw, :agg, :prop)

and to inspect the specific attributes of one agent type by typing

fieldnames(typeof(model.bank))
(:E_k, :Pi_k, :Pi_e_k, :D_k, :r, :Y_h, :C_d_h, :I_d_h, :C_h, :I_h, :K_h, :D_h)

We can now define a data tracker, which will store the time series of the model.

data = Bit.init_data(model);

We can run now the model for a number of epochs and progressively update the data tracker.

for t in 1:T
+model = Bit.init_model(parameters, initial_conditions, T)
Model(Workers{Vector{Float64}, Vector{Int64}}([0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249  …  4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569], [3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487  …  22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958], [6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249  …  42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032], [0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983  …  11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522], [1, 2, 3, 4, 4, 5, 6, 6, 6, 6  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), Workers{Vector{Float64}, Vector{Int64}}([2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714  …  2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714], [13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984  …  13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984], [25.74799087814977, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.3061857958470848, 1.3061857958470848, 1.3061857958470848, 2.02208568733654, 1.3061857958470848, 7.033384927762728, 1.3061857958470848, 1.3061857958470848, 1.3061857958470848, 1.3061857958470848  …  2.631055234104208, 2.631055234104208, 2.631055234104208, 2.631055234104208, 2.631055234104208, 2.631055234104208, 27.120287191063134, 2.631055234104208, 2.631055234104208, 2.631055234104208], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [11.88921238727358, 11.88921238727358, 11.88921238727358, 18.405502707537238, 11.88921238727358, 64.01953494938287, 11.88921238727358, 11.88921238727358, 11.88921238727358, 11.88921238727358  …  23.94848771160181, 23.94848771160181, 23.94848771160181, 23.94848771160181, 23.94848771160181, 23.94848771160181, 246.85527544670435, 23.94848771160181, 23.94848771160181, 23.94848771160181], [6.447669663541784, 6.447669663541784, 6.447669663541784, 9.981535999529559, 6.447669663541784, 34.718600351443996, 6.447669663541784, 6.447669663541784, 6.447669663541784, 6.447669663541784  …  12.987566600381651, 12.987566600381651, 12.987566600381651, 12.987566600381651, 12.987566600381651, 12.987566600381651, 133.87272587431335, 12.987566600381651, 12.987566600381651, 12.987566600381651]), Bank{Float64}(89460.0, 6476.292527744359, 0.0, 126431.00000000006, 0.028359903595743693, 3695.369633667522, 0.0, 0.0, 0.0, 0.0, 33636.12938055408, 18241.296726948152), CentralBank{Float64}(0.0016459319014481277, 0.0089810924595537, 0.9259668580654086, -0.003424572940686137, 0.0049629315732038215, 0.30996974466133875, 1.328593153520194, 106179.90000000002), Government{Float64}(0.9905949533296431, 0.09373211872949586, 0.011235005057648862, 0.0, 14732.121510837034, 232610.9, 2.238468336136841, 0.5902859043576301, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0, 0.0), RestOfTheWorld{Float64}(0.962809216044625, 0.39260026877953946, 0.020320381298662014, 0.9662360466537488, 0.35492769963078624, 0.02122821278168188, 2.3548476e6, 0.0, 0.0019383188997990075, 0.38456173629534834, 0.0026219533879005877, 0.0025327891562467505, 0.9635784504324201, 0.5360029623199525, 0.006618207536795881, 0.0, 33097.63671130043, 34095.03119997918, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0), Aggregates{Float64, Int64}([104531.39273728609, 105062.38754395355, 105399.12953350678, 106689.88106040593, 107938.33111423723, 108890.48532381697, 110110.17779727321, 110374.00540561741, 110808.89423399912, 111932.48072916963  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.007497362866886709, -0.007895434153021436, -0.0019938777296781562, -0.0035311300388783107, 0.001212170001002849, -0.001672412335241874, 0.001839696090252002, 0.004290005139261838, 0.00600429551344886, 0.0036060572293247772  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1.0, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0  …  1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1), MutableNamedTuple(tau_VAT = 0.1528683933530887, tau_EXPORT = 0.0029486201783457183, tau_SIW = 0.17114894621657745, psi_H = 0.07125099957246343, tau_FIRM = 0.07701197259426128, H_inact = 4130, theta_DIV = 0.7858074440019603, I_s = [48, 2, 1, 1, 5, 2, 4, 1, 1, 1  …  14, 10, 13, 41, 20, 16, 7, 7, 2, 19], psi = 0.9096681249468772, tau_INC = 0.21340742230566648, zeta_b = 0.5, tau_CF = 0.08761417854834112, H_act = 4743, zeta = 0.03, mu = 0.026713971694295565, tau_G = 0.009147800682711324, theta_UB = 0.3585824478060919, T_prime = 54, tau_SIF = 0.21215146534992413, T = 16, zeta_LTV = 0.6, I = 624, products = MutableNamedTuple(a_sg = [0.37790282216028437 0.0 … 0.0 0.0; 0.0006712800413285777 0.8149348034258406 … 2.4029219530949635e-5 0.00019143106686926454; … ; 0.00037430822580994426 0.000357977071568566 … 0.3197327950788158 0.0011366219595362582; 0.0 5.36965607352849e-5 … 0.0 0.05594572929254256], b_CF_g = [0.0033476048872100555, 0.0, 0.0, 0.0008050086095806136, 0.0, 0.003306696048303853, 0.0030629933432974495, 0.0, 0.0, 0.0  …  0.0017883064872300512, 0.0, 0.0, 0.0, 0.0, 0.002291709084994238, 0.0, 0.0, 0.0, 0.0], b_CFH_g = [0.0006092803753845975, 0.0, 0.0, 0.004372477702289994, 0.0, 0.0, 0.06710603871527036, 0.0, 0.0, 0.0  …  0.0041711096896454745, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], c_E_g = [0.005090338238241512, 0.0005933402816643566, 1.793599921320476e-5, 0.007268146142708006, 0.05434404438533037, 0.02138421321578873, 0.023894091259534518, 0.028037089231640524, 0.005923381894006229, 0.011927802553688313  …  0.0014618202435669027, 0.0009447261124040238, 0.0001280209174610526, 0.0010477673386531637, 0.0, 0.0013030104043795392, 5.5260305268213854e-5, 0.0, 1.307076865739618e-5, 8.641230390167474e-6], b_HH_g = [0.011287997598927976, 0.0020040637862256817, 0.0003326837475323491, 0.00034402684015257527, 0.06305103828047173, 0.03068200872784047, 0.0003505790613555631, 0.0020473225369636873, 0.0, 0.0191077565617325  …  0.005591228759882936, 0.0004356874830029748, 0.014434120586233577, 0.03514505772295519, 0.024907176866291014, 0.013342154173060372, 0.009686226103767459, 0.011051272187723254, 0.0021206651420422927, 0.01721782824162338], c_G_g = [0.0, 0.0, 0.0, 0.0, 8.57086390274792e-6, 0.0, 0.0, 0.0, 2.4536198623552867e-5, 0.0  …  0.008555738848801895, 0.3324338967920097, 0.22067672180252998, 0.2370889178393625, 0.0477595425645907, 0.012942508661614227, 0.004667927760053456, 0.021757222045203074, 0.0, 0.002000708524749294], c_I_g = [0.016810689305736877, 0.004087420487057966, 0.00036885674795364003, 0.05818437780960789, 0.04895082866561155, 0.04689140072807505, 0.010101572733480902, 0.016066325760592727, 0.00037371898454196314, 0.041008926225895866  …  0.0018123421762754539, 0.000401097116716159, 0.0003479865324437806, 0.0006664256271585853, 0.0028556289501379516, 0.001307065586721844, 6.418152296577174e-5, 0.0, 3.538212163497461e-5, 0.00020458795490837936]), J = 156, G = 62, H = 8873, L = 312, theta = 0.05, C = [4.3800671000101816e-5 0.00010629745355671226 9.959785873214212e-5; 0.00010629745355671226 0.0004129178961230129 0.0003596689472264872; 9.959785873214212e-5 0.0003596689472264872 0.0004506370179043619]))

Note that the it is very simple to inspect the model by typing

fieldnames(typeof(model))
(:w_act, :w_inact, :firms, :bank, :cb, :gov, :rotw, :agg, :prop)

and to inspect the specific attributes of one agent type by typing

fieldnames(typeof(model.bank))
(:E_k, :Pi_k, :Pi_e_k, :D_k, :r, :Y_h, :C_d_h, :I_d_h, :C_h, :I_h, :K_h, :D_h)

We can now define a data tracker, which will store the time series of the model.

data = Bit.init_data(model);

We can run now the model for a number of epochs and progressively update the data tracker.

for t in 1:T
     Bit.run_one_epoch!(model; multi_threading = true)
     Bit.update_data!(data, model)
-end

Note that we can equivalently run the model for a number of epochs in the single command data = BeforeIT.run_one_sim!(model), but writing the loop explicitely is more instructive.

We can then plot any time series stored in the data tracker, for example

plot(data.real_gdp, title = "gdp", titlefont = 10)
Example block output

Or we can plot multiple time series at once using the function plot_data

ps = Bit.plot_data(data, quantities = [:real_gdp, :real_household_consumption, :real_government_consumption, :real_capitalformation, :real_exports, :real_imports, :wages, :euribor, :gdp_deflator])
-plot(ps..., layout = (3, 3))
Example block output

To run multiple monte-carlo repetitions in parallel we can use

model = Bit.init_model(parameters, initial_conditions, T)
-data_vector = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999997, 73335.95100821069, 73597.77249141877, 75487.96540571815, 76498.87159269878, 76446.77847342764, 77176.56525808257, 77920.32927671348, 79550.55634458609, 79740.41315984342, 79824.49497829715, 79889.47675207978, 79687.98719499444, 79554.8486959027, 80917.92470968528, 81797.01453254609, 82044.50122137633], [72421.99999999997, 72690.13451480451, 72578.84148895051, 73520.37361632683, 73662.40182576534, 73602.20651186592, 73393.54044373099, 73662.98983068875, 74904.75716562259, 75167.11338661965, 74966.68999350391, 74688.00668378884, 74602.06826290015, 74656.6127999981, 75184.7512147223, 75679.72230008256, 75493.64286208722], [64900.92049553803, 65736.26728472294, 65960.17640576059, 67677.24182490444, 68581.67087315927, 68523.58684604274, 69187.17897929573, 69877.39198558737, 71353.13200580869, 71512.75375583352, 71568.2450126374, 71616.5058837039, 71440.82466379367, 71330.64700841092, 72567.38811297198, 73365.32409798009, 73570.29003364482], [64900.92049553803, 65157.37569003048, 65046.98479149074, 65913.2363357163, 66038.75969097574, 65973.83553224937, 65795.77623886024, 66059.49516652864, 67186.06721932063, 67411.32955229227, 67212.882946522, 66953.67509679469, 66881.26361235278, 66938.77974472906, 67425.86443177718, 67878.60640050017, 67696.0566324104], [40512.94792630535, 40929.605040029455, 41194.18488314596, 42102.496535514874, 42691.95730415226, 42698.84627915105, 42988.93612290118, 43120.07445735753, 43955.05927268153, 44107.5771207238, 44319.99469034568, 44446.74935181001, 44333.71450489033, 44198.09405636835, 44830.935928146966, 45260.1366395446, 45511.657814470374], [40512.94792630535, 40569.16771508781, 40623.86827330915, 41005.096094915876, 41109.000017812374, 41110.029283299074, 40881.71339859785, 40764.11939907096, 41388.05801381205, 41577.904092327146, 41622.85402955847, 41552.89592097934, 41504.208002501946, 41476.79303718077, 41654.587368297296, 41875.30060503187, 41877.77108713727], [14866.888022051655, 14729.504948351363, 14864.816484503159, 15049.473678499275, 15472.588217175131, 15769.036288503938, 15975.167482103428, 16441.63766023256, 16811.30608534332, 16848.10607440329, 16664.73438195338, 16560.43411314823, 16511.22252837565, 16683.044406415964, 17112.354628937366, 17043.014475001437, 17067.00054678291], [14866.888022051655, 14599.79288892382, 14659.019191333353, 14657.209551557607, 14898.886569288115, 15182.273060767393, 15192.100047110385, 15543.314549718292, 15829.515943357603, 15881.82766379527, 15650.584154345706, 15482.211976022974, 15457.428321682974, 15655.86016429841, 15899.915007592423, 15768.431280718636, 15704.282734674935], [15944.236265986641, 16444.203953492884, 16230.551972797892, 17053.030053579387, 16862.196323294484, 16574.019987339758, 16742.96600132077, 16961.327466870225, 17861.148545514992, 18227.298337049233, 17940.154158827034, 17250.56525369547, 17142.329730930065, 17079.214734896017, 17504.994468545567, 18281.317284094792, 17932.899590338417], [15944.236265986641, 16299.391784453022, 16005.846631421093, 16608.54328357134, 16236.969975776881, 15957.303449536405, 15922.262778301816, 16034.610021603623, 16818.04698769983, 17181.920014463743, 16848.386897106146, 16127.409833531057, 16048.25642844597, 16027.638067229764, 16264.735642375663, 16914.126062504463, 16501.043909106585], [15944.236265986641, 16143.068743571095, 16218.296141567576, 16629.29668844752, 16869.556651703082, 16853.140945187195, 17002.093903222725, 17105.747129830106, 17441.758350611257, 17494.678567491203, 17539.906431268988, 17565.14684968272, 17522.1541393186, 17501.587317479007, 17771.91543949303, 17940.257604297156, 18042.60405281688], [15944.236265986641, 16000.908453761529, 15993.76047715217, 16195.854517212847, 16244.057393636984, 16226.03836278612, 16168.688802636301, 16171.13901554801, 16423.149426319727, 16491.317707484726, 16472.49667292613, 16421.509548523634, 16403.839339245067, 16424.0049370326, 16512.74480557007, 16598.572958245746, 16601.98899850849], [3173.2320350842087, 3207.6875316105325, 3223.942053136019, 3297.8255914746273, 3346.050152994346, 3343.001021884486, 3372.72650849628, 3356.8280828610955, 3418.0179490487644, 3444.42236407809, 3471.4241778206874, 3481.352409977149, 3472.6098606575774, 3464.128696198674, 3491.6094428469823, 3506.7921350824795, 3561.2148926975133], [3173.2320350842087, 3179.4397556544313, 3179.3078964638908, 3211.8678560693133, 3221.983354360451, 3218.608507716857, 3207.4029024120273, 3173.4207905236003, 3218.403694780204, 3246.876660559484, 3260.166948070172, 3254.687383569933, 3250.9778072480917, 3250.840382464157, 3244.2229362810667, 3244.532290864465, 3276.869031588493], [34195.564496956766, 34359.844353529166, 34651.303917740544, 34910.06065390013, 35149.87332769689, 35445.2677065282, 35741.295576346, 37952.84213023077, 37470.03146592674, 36795.08179766944, 37186.83553576928, 37946.386491045734, 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Note that this will use the number of threads specified when activating the Julia environment. To discover the number of threads available, you can use the command

Threads.nthreads()
2

To activate Julia with a specific number of threads, say 8, you can use the command julia -t 8 in the terminal.

We can then plot the results of the monte-carlo repetitions using the function plot_data_vector

ps = Bit.plot_data_vector(data_vector)
-plot(ps..., layout = (3, 3))
Example block output +end

Note that we can equivalently run the model for a number of epochs in the single command data = BeforeIT.run_one_sim!(model), but writing the loop explicitely is more instructive.

We can then plot any time series stored in the data tracker, for example

plot(data.real_gdp, title = "gdp", titlefont = 10)
Example block output

Or we can plot multiple time series at once using the function plot_data

ps = Bit.plot_data(data, quantities = [:real_gdp, :real_household_consumption, :real_government_consumption, :real_capitalformation, :real_exports, :real_imports, :wages, :euribor, :gdp_deflator])
+plot(ps..., layout = (3, 3))
Example block output

To run multiple monte-carlo repetitions in parallel we can use

model = Bit.init_model(parameters, initial_conditions, T)
+data_vector = Bit.run_n_sims(model, 4)
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3174.533343043218, 3148.4205671047807, 3142.083429681925, 3158.3386585034877, 3207.6297870989793, 3218.8118451666865, 3247.906522249298, 3238.785756970948, 3220.488353892282, 3266.55270810488, 3256.9199760862816, 3227.1291770312, 3267.6102236547404], [34195.564496956766, 33920.83393081291, 34787.03429924323, 34679.21643622916, 34813.25782674476, 35439.99018268574, 35744.2614099271, 34665.854567065384, 34868.58635034721, 36818.09844954177, 35536.54060342287, 36037.106131450644, 36124.79433917417, 36138.70071789997, 36998.42376364486, 36689.233851729026, 37532.68900114891], [34195.564496956766, 33504.22083448171, 34342.33291832261, 34234.77794401712, 34241.54238043027, 35018.049316733246, 35449.19544515323, 34430.91075681396, 34466.29316755571, 36146.66966949259, 34929.70417600852, 35410.77167667172, 35691.25442107769, 35748.16130093238, 36418.008669149116, 36040.82407061874, 36590.042281771865], [33097.63671130043, 33018.660787013825, 33407.99390763431, 33084.102620465914, 33164.37480169929, 33479.210176728324, 33583.43308909793, 32288.03417729544, 32530.169471463632, 34475.72348226798, 33399.29357560848, 33713.661186467856, 33588.92484706733, 33513.66959809318, 34333.258835805245, 33940.723507056544, 34621.36122696488], [33097.63671130043, 32613.128112456205, 32980.92154220315, 32660.106631056307, 32619.737886767183, 33080.6139338807, 33306.20458600449, 32069.20576905684, 32154.85556335359, 33847.01113877921, 32828.95364810206, 33127.70882057022, 33185.818338268335, 33151.49805552857, 33794.653683357836, 33340.888220528774, 33751.833531254975], [29576.147776884874, 30035.035200337115, 30179.620599229707, 30166.173691611166, 30729.51785766715, 30600.17906038357, 30685.283528429365, 30914.39555091114, 31579.701384509684, 31773.041609461856, 31956.77963156495, 31723.107704995768, 31712.322580216634, 32254.913767648413, 32170.002176826107, 32411.779752010974, 33087.13218978104], [34346.71094688275, 34879.615833934775, 35015.501292004505, 35018.87193020228, 35688.737554256666, 35522.70644522067, 35614.54795642681, 35878.19789033599, 36699.85954768616, 36885.61415036286, 37162.64459319482, 36881.036228214914, 36915.59913050572, 37578.03117097287, 37376.65803490076, 37811.34621807423, 38564.807971570735], [28335.32931213966, 28774.96487113161, 28887.067576077403, 28889.848283187137, 29442.473630103676, 29305.50138382745, 29381.268739501622, 29598.77450626904, 30276.62845508473, 30429.872177496163, 30658.416588612297, 30426.09548598623, 30454.60916870559, 31001.10196222453, 30834.973271356695, 31193.5820719887, 31815.17250440137], [978.0617717704339, 993.2368462869223, 994.9609061433653, 994.8756669419977, 1011.0565641236809, 1003.771147590453, 1006.5617352712663, 1014.8716544359955, 1031.2320190890894, 1032.8958722868665, 1047.390418525055, 1036.877399924902, 1039.5980781465757, 1058.774656955542, 1050.5361281874943, 1064.5979048223858, 1086.9208697668703], [0.0019383188997990075, 0.0011929493317586193, 0.0006466413446331032, 0.004435908615390227, 0.0028931523898745315, 0.004229168731186617, -0.0003910790573121714, 0.0021955412677587827, 0.006408213820023878, 0.0044650654235618426, 0.0008229671752246759, 0.0018854372134908814, 0.006675158251289837, 0.00835311480062062, 0.008125665168410956, 0.008811312799200888, 0.010076920792365884], [2.3548476e6, 2.3470227710231873e6, 2.3709522501873304e6, 2.3868592841351903e6, 2.3646089561258047e6, 2.389084788376763e6, 2.381073068032339e6, 2.381454540616725e6, 2.385463434709314e6, 2.4107033030581404e6, 2.3883825077566784e6, 2.3992245851179813e6, 2.4153397559361043e6, 2.4098052862332095e6, 2.4264988579710866e6, 2.427839359400001e6, 2.4532885685983375e6], [0.0016459319014481277, 0.001224617967877698, 0.002151640539855209, 0.002754053033193182, 0.001699641706559376, 0.0026889768105918207, 0.002151082011084415, 0.002057972286657964, 0.0022182472203537166, 0.003197204556189501, 0.002068672574413688, 0.0024052948198060413, 0.003041071417767443, 0.002782238597196384, 0.003444102692906453, 0.0034456654726730163, 0.00445285048065099], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 526.1217399118136 262.2500284494269 … 139.12280285142342 512.7384551397362; … ; 577.3380778236512 288.0211543975216 … 147.35858742764964 537.4582684322764; 588.2073503388793 295.5822561931196 … 150.60968087722893 549.1939702961307], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 519.659952811919 259.02909359304573 … 137.4141071930435 506.44104052311644; … ; 567.1347670055209 282.93094906827537 … 144.7543152914469 527.959754519491; 573.4343153161709 288.15860359796505 … 146.82706563266188 535.4007700704434])])

Note that this will use the number of threads specified when activating the Julia environment. To discover the number of threads available, you can use the command

Threads.nthreads()
2

To activate Julia with a specific number of threads, say 8, you can use the command julia -t 8 in the terminal.

We can then plot the results of the monte-carlo repetitions using the function plot_data_vector

ps = Bit.plot_data_vector(data_vector)
+plot(ps..., layout = (3, 3))
Example block output diff --git a/dev/examples/change_expectations-27ebd9b8.svg b/dev/examples/change_expectations-ee7f7e29.svg similarity index 86% rename from dev/examples/change_expectations-27ebd9b8.svg rename to dev/examples/change_expectations-ee7f7e29.svg index 8c09264..fbdb553 100644 --- a/dev/examples/change_expectations-27ebd9b8.svg +++ b/dev/examples/change_expectations-ee7f7e29.svg @@ -1,82 +1,82 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/change_expectations-28db5f2d.svg b/dev/examples/change_expectations-f9f023d7.svg similarity index 85% rename from dev/examples/change_expectations-28db5f2d.svg rename to dev/examples/change_expectations-f9f023d7.svg index 31639e7..23bb2ba 100644 --- a/dev/examples/change_expectations-28db5f2d.svg +++ b/dev/examples/change_expectations-f9f023d7.svg @@ -1,284 +1,284 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/change_expectations.html b/dev/examples/change_expectations.html index 83f87da..9bcdd01 100644 --- a/dev/examples/change_expectations.html +++ b/dev/examples/change_expectations.html @@ -35,7 +35,7 @@ p2 = plot(data.real_household_consumption, title = "consumption", titlefont = 10) plot!(p2, data_back.real_household_consumption, titlefont = 10, label = "backward looking") -plot(p1, p2, layout = (2, 1), legend = true)Example block output

Plot all time series

p1 = plot(data.real_gdp, title = "gdp", titlefont = 10)
+plot(p1, p2, layout = (2, 1), legend = true)
Example block output

Plot all time series

p1 = plot(data.real_gdp, title = "gdp", titlefont = 10)
 plot!(p1, data_back.real_gdp, titlefont = 10)
 p2 = plot(data.real_household_consumption, title = "household cons.", titlefont = 10)
 plot!(p2, data_back.real_household_consumption, titlefont = 10)
@@ -54,4 +54,4 @@
 p9 = plot(data.nominal_gdp ./ data.real_gdp, title = "gdp deflator", titlefont = 10)
 plot!(p9, data_back.nominal_gdp ./ data_back.real_gdp, titlefont = 10)
 
-plot(p1, p2, p3, p4, p5, p6, p7, p8, p9, layout = (3, 3), legend = false)
Example block output

Note that, importantly, once the function estimate_next_value has been changed, the model will use the new expectations in all the simulations, unless the function is changed again. To restore the original expectations you could close the Julia session.

+plot(p1, p2, p3, p4, p5, p6, p7, p8, p9, layout = (3, 3), legend = false)Example block output

Note that, importantly, once the function estimate_next_value has been changed, the model will use the new expectations in all the simulations, unless the function is changed again. To restore the original expectations you could close the Julia session.

diff --git a/dev/examples/get_parameters_and_initial_conditions.html b/dev/examples/get_parameters_and_initial_conditions.html index a5e8026..4ad3eb8 100644 --- a/dev/examples/get_parameters_and_initial_conditions.html +++ b/dev/examples/get_parameters_and_initial_conditions.html @@ -31,4 +31,4 @@ ".jld2", init_conds, ) -end +end diff --git a/dev/examples/multithreading_speedup.html b/dev/examples/multithreading_speedup.html index 369e075..539c115 100644 --- a/dev/examples/multithreading_speedup.html +++ b/dev/examples/multithreading_speedup.html @@ -7,5 +7,5 @@ @time data = Bit.run_one_sim!(model; multi_threading = false); model = Bit.init_model(parameters, initial_conditions, T); -@time data = Bit.run_one_sim!(model; multi_threading = true);
  5.153831 seconds (3.52 M allocations: 6.415 GiB, 18.08% gc time)
-  2.538049 seconds (3.41 M allocations: 6.146 GiB, 8.92% gc time)

Is the speedup in line to what we would expect? Yes!

+@time data = Bit.run_one_sim!(model; multi_threading = true);
  5.257994 seconds (3.52 M allocations: 6.415 GiB, 18.29% gc time)
+  2.779143 seconds (3.43 M allocations: 6.257 GiB, 14.86% gc time)

Is the speedup in line to what we would expect? Yes!

diff --git a/dev/examples/scenario_analysis_via_overload-5466a8b9.svg b/dev/examples/scenario_analysis_via_overload-5466a8b9.svg new file mode 100644 index 0000000..e35f382 --- /dev/null +++ b/dev/examples/scenario_analysis_via_overload-5466a8b9.svg @@ -0,0 +1,48 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/scenario_analysis_via_overload-7d3afc74.svg b/dev/examples/scenario_analysis_via_overload-7d3afc74.svg deleted file mode 100644 index 89be5df..0000000 --- a/dev/examples/scenario_analysis_via_overload-7d3afc74.svg +++ /dev/null @@ -1,46 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/scenario_analysis_via_overload.html b/dev/examples/scenario_analysis_via_overload.html index 62f466a..5b89362 100644 --- a/dev/examples/scenario_analysis_via_overload.html +++ b/dev/examples/scenario_analysis_via_overload.html @@ -25,7 +25,7 @@ "D_I" => 54049.0 ⋮ => ⋮

Initialise the model and the data collector

T = 20
 model = Bit.init_model(parameters, initial_conditions, T);
-data = Bit.init_data(model);

Simulate the model for T quarters

data_vec_baseline = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999999, 72490.32715969233, 72745.01350851887, 73036.19868684297, 73101.97101584724, 73135.88215937368, 73338.00877368738, 73508.2692774224, 73822.52497256578, 74078.54126491581  …  74538.2788285288, 74675.61968507324, 74801.27856331534, 74838.48637448097, 75022.55143235985, 75162.20985954536, 75357.51802312113, 75480.63869547483, 75830.51292712508, 76421.36837097253], [72421.99999999999, 72417.29669592284, 72598.51324598843, 72815.6797901376, 72807.829229122, 72768.2195657863, 72895.81703027921, 72991.44147788413, 73229.6379957611, 73409.56702209338  …  73716.39677910489, 73777.82076542922, 73827.51626696206, 73789.82509366258, 73896.78848929412, 73959.76525690223, 74077.24441510497, 74123.52221381417, 74392.08385280793, 74896.20100186537], [64900.92049553802, 64966.37094093033, 65216.06270399384, 65470.60715239121, 65512.06600920255, 65559.83609434035, 65741.53158208827, 65889.20588388809, 66184.54135292272, 66416.058623472  …  66810.49076820773, 66923.94942845879, 67036.8797458268, 67068.29571476165, 67238.29935374917, 67364.09825784479, 67553.73811903397, 67655.28002546652, 67980.5731836729, 68537.47954022205], [64900.92049553802, 64900.920495538005, 65084.72490025257, 65272.93111892486, 65248.46386715041, 65230.25916627262, 65345.14282064378, 65425.9468026305, 65652.99691376244, 65816.28125515157  …  66073.81769313029, 66119.34613559599, 66164.19431563858, 66128.51287983608, 66229.34425528847, 66286.40779992139, 66406.04548921435, 66438.86085916402, 66691.04962407211, 67169.65363519284], [40512.94792630534, 40509.74867899371, 40428.33705746146, 40676.07232946786, 40783.93227281672, 40687.696455344994, 40859.781105636655, 40981.96126643634, 41042.59315818606, 41169.41067171808  …  41473.54754927279, 41573.89862920543, 41640.39700630889, 41698.480175325676, 41777.21045382879, 41863.7788287501, 41830.5770044632, 41955.60917679909, 42064.793923470614, 42132.624716022554], [40512.94792630534, 40468.93708592837, 40346.918940852105, 40553.25867331277, 40619.82919130329, 40483.1546686845, 40613.41693316324, 40693.82202618083, 40712.96993022377, 40797.6258759961  …  41016.24742385546, 41074.07015195804, 41098.3227344687, 41114.18747049956, 41150.31581354211, 41194.04233493299, 41119.90360183771, 41201.26144344637, 41266.86680615273, 41291.769523797004], [14866.888022051655, 14924.433192075354, 15178.264350186813, 15225.342731053921, 15261.570809239949, 15052.826337437726, 15043.546478547543, 15315.324315781883, 15332.566728847976, 15461.844654385159  …  15774.694870888425, 15964.269774665834, 15818.759370401805, 15880.40737271453, 15850.785525080835, 15930.826102461382, 15897.24955673825, 15997.157781051972, 16108.41970913961, 16370.700000502726], [14866.888022051655, 14909.397554629351, 15147.697035606572, 15179.372707402927, 15200.16253742891, 14977.15402709377, 14952.841370028464, 15207.644112681632, 15209.427089044244, 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14878.646654229799, 14942.437977003809, 14869.414990795824, 15026.7421595825, 15130.837386418669, 15084.298914197441, 15182.337452366985, 15166.036717101484, 15106.804550458077  …  15218.751901219934, 15310.707382124176, 15389.242355398903, 15394.907155109924, 15495.2762042651, 15524.587816413368, 15343.032712141185, 15498.053292048251, 15554.68087895252, 15460.163756353311], [15944.236265986641, 15961.61492961959, 16031.078209895408, 16131.530638804341, 15926.130946674342, 16204.332097217404, 16243.747929712286, 16210.7163917413, 16367.541766783137, 16460.844289251363  …  16564.643517085515, 16924.44993691182, 16285.294403281863, 16943.911928520156, 16432.19537217406, 16775.908196497465, 16718.556586195187, 16023.772805702662, 16574.165162565452, 16699.165972480547], [15944.236265986641, 15945.534382234917, 15998.793424270621, 16082.824553293363, 15862.048671638817, 16122.871033368692, 16145.806202951086, 16096.740794654856, 16236.089986185803, 16312.192862581942  …  16381.996649251088, 16720.973180602225, 16073.292603586631, 16706.488308056672, 16185.619425741628, 16507.52731329815, 16434.519540881876, 15735.67076326045, 16259.769807290942, 16365.895009457461], [15944.236265986641, 15955.09452673948, 15976.867298005945, 16037.108903406312, 16074.816867253645, 16161.926129829393, 16212.911606617421, 16278.892941781645, 16337.340228894356, 16405.965552668942  …  16476.530320398586, 16512.312077218197, 16518.174283067838, 16563.098219620297, 16556.492285225468, 16599.62827914377, 16626.038529027774, 16626.285392703438, 16674.96946046684, 16762.176565070673], [15944.236265986641, 15939.020548342216, 15944.69168705088, 15988.687906348583, 16010.136321860531, 16080.678245715351, 16115.155807558616, 16164.438003572308, 16206.131004325829, 16257.809714338942  …  16294.855015844296, 16313.790310592485, 16303.14085542626, 16331.010685054249, 16308.051181503935, 16334.067521052171, 16343.57330335454, 16327.35037671518, 16358.661948366267, 16427.648078114486], [3173.2320350842083, 3171.2111577810924, 3148.6897923950432, 3155.852968870274, 3154.657054245484, 3159.9682957684495, 3170.987421964578, 3183.7786181127826, 3188.6540749253186, 3216.2573638200897  …  3241.0306435514676, 3257.5532351170673, 3260.9810184723096, 3267.0422500037043, 3272.6636580690333, 3283.131016158452, 3290.4280472180662, 3292.952963478067, 3245.2290027896383, 3272.7978923498194], [3173.2320350842083, 3168.0163174397912, 3142.3486858508995, 3146.3244716678973, 3141.9636008468005, 3144.08276728402, 3151.867943812791, 3161.393853601369, 3163.0452045261895, 3187.2126054063256  …  3205.2940401654973, 3218.388809197272, 3218.529600182395, 3221.2633883998847, 3223.5551780049573, 3230.607504974738, 3234.5258851187605, 3233.7467773976855, 3183.6702506430374, 3207.48154618254], [34195.564496956766, 33929.75297940831, 34290.80576458739, 33839.50449363778, 33884.13281319115, 33949.76318510454, 34584.41374570209, 34390.0939982841, 34631.40510156556, 34658.61096425066  …  35035.45762899092, 34836.921037561166, 35910.51981264336, 35613.526872660506, 36793.569619423775, 36226.06136572447, 37640.830800281416, 38398.94217902295, 37313.61811683909, 37303.872663516406], [34195.564496956766, 33895.57041060552, 34221.747944613206, 33737.3325524612, 33747.79258557748, 33779.09377337697, 34375.886919508135, 34148.301383905266, 34353.271712321555, 34345.62264629539  …  34649.14586849451, 34418.08889123085, 35443.03702481378, 35114.49851821374, 36241.4578019791, 35646.517043575914, 37001.33837110375, 37708.542121337436, 36605.81605807131, 36559.386526451315], [33097.63671130043, 32769.17729583597, 32981.258580625836, 32465.540340100546, 32413.325498226917, 32539.09826205012, 33037.556084139556, 32810.795814550955, 33032.9060849854, 33063.53371820994  …  33649.409988581734, 33851.678577995976, 34423.739350621254, 34650.899063103716, 35553.74914149182, 35259.89872990082, 36298.26589045614, 36624.5340660457, 35741.742781955385, 35604.268465621564], [33097.63671130043, 32736.163950345213, 32914.83804116056, 32367.51682209902, 32282.903385891674, 32375.52042711058, 32838.35604084587, 32580.107055740173, 32767.61063713824, 32764.94991706472  …  33278.381216815455, 33444.691657986084, 33975.61145597903, 34165.35936350055, 35020.241649326184, 34695.81107206392, 35681.582737196666, 35966.037269076216, 35063.757627961466, 34893.70191045202], [29576.14777688487, 29605.974349916592, 29688.302509343186, 29821.90892685067, 29904.72661141212, 30065.215576851573, 30162.359244405514, 30299.138704643658, 30406.186557800414, 30511.770742677672  …  30630.04464931671, 30662.228210051824, 30661.6928960753, 30747.61598035315, 30734.35781747913, 30817.633862170867, 30852.424965941733, 30884.607646790053, 31096.584466326418, 31215.317590523555], [34346.71094688275, 34381.34847608994, 34526.41393109727, 34640.34155181819, 34688.89962782172, 34976.135462193626, 35046.70572014448, 35179.126026269376, 35338.47322750703, 35426.202440588335  …  35546.37512151451, 35634.326293050784, 35658.66085698628, 35826.33369817297, 35772.97266933772, 35890.6071230476, 35991.4927233348, 35944.73823934199, 36225.53174137875, 36368.90729775711], [28335.329312139664, 28363.904560528434, 28483.580573926196, 28577.568515184037, 28617.62792804752, 28854.591577049086, 28912.8105867753, 29022.05461271612, 29153.51277268432, 29225.887567080154  …  29325.027554417862, 29397.585459968785, 29417.660974152543, 29555.987615648854, 29511.965865594837, 29609.011867742698, 29692.240410685616, 29653.66892409395, 29885.31778157044, 30003.599663395304], [978.0617717704338, 979.0481149238876, 981.5141209144572, 984.0835300706825, 981.4898664162275, 988.3876135339045, 990.4383257253617, 995.5843065851361, 998.8842157122388, 1001.6504453317851  …  1008.2404950277506, 1011.4931732723699, 1013.7305642084975, 1022.235068461972, 1025.4223342924051, 1030.3546572052733, 1034.3454963299118, 1032.8911765693085, 1042.7587749658642, 1043.3181695162555], [0.0019383188997990075, -0.00019383731229871692, 0.0015865064634743575, 0.0031133137585348525, 0.004399791137177145, 0.002166087409281703, 0.0016740035053637303, 0.005956015446816565, 0.007373165185340591, 0.0059954835365734205  …  0.00504496730018178, 0.0016683759367552842, 0.005209366237136193, 0.004357491062291441, 0.0026441753971822024, 0.002048543154599569, 0.005012000691778429, -0.00014281457528020347, 0.0040835279373843125, 0.0019471274808173789], [2.3548476e6, 2.350025002505579e6, 2.379121545873197e6, 2.368350448881972e6, 2.3823400539233875e6, 2.3830945574325714e6, 2.407709015963127e6, 2.3821542978136973e6, 2.37026611056626e6, 2.3785481779057784e6  …  2.4047194428663715e6, 2.406318784335948e6, 2.4394224984722026e6, 2.433896855958284e6, 2.4520685903497613e6, 2.4298229510838436e6, 2.453051201446873e6, 2.4557512103466266e6, 2.4338445156196784e6, 2.42493033351985e6], [0.0016459319014481277, 0.001318194524972672, 0.002474842876542719, 0.0019177578871649246, 0.002457748870876194, 0.002356652713714306, 0.003236535393778621, 0.0020896399206917684, 0.0016132698411041734, 0.0019751033837272936  …  0.0029805792478714388, 0.0028636212046422456, 0.004124299991912632, 0.0036961615927324346, 0.004217567493997267, 0.0030599975410188136, 0.0038887580657931643, 0.0037058459556543045, 0.0026477731079176574, 0.0021361805101518623], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 518.6059091613057 258.5036962251161 … 137.13538556670352 505.41380923410753; … ; 527.076581551166 264.9473826341032 … 143.21428694203647 527.2347647057791; 535.6500268309849 270.0350596777946 … 143.939012257057 529.281686682483], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 518.0834390395163 258.24326637797435 … 136.99722836422478 504.904629508617; … ; 517.0784653571795 259.9215954722596 … 140.4976549921376 517.2336479353772; 524.9598761624129 264.6458776948668 … 141.06637219164566 518.718631154929])])

Now, apply a shock to the model and simulate it again Here, we do this by overloading the centralbankrate function with the wanted behaviour

import BeforeIT: central_bank_rate
+data = Bit.init_data(model);

Simulate the model for T quarters

data_vec_baseline = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72422.0, 72490.27837163064, 72674.53691411068, 72844.26048284073, 73252.18066484289, 73563.52776880116, 73656.54849633592, 73931.9293575247, 74083.24767036656, 74319.17529096817  …  74143.19251120229, 74146.60914283775, 74034.12185660837, 74344.17985850594, 74707.86256419588, 75239.4907035573, 75489.02850079922, 76196.17898017944, 76637.15674495003, 77010.85011331986], [72422.0, 72417.24795701286, 72528.17858350225, 72624.3211069996, 72957.43447670525, 73193.71535095164, 73212.43611671659, 73412.12285492191, 73488.26676501962, 73648.02797666454  …  73325.66680535488, 73255.17033236029, 73070.34639859153, 73302.44496181494, 73586.82173547549, 74035.80976241404, 74206.52061811236, 74826.19732500578, 75183.42644325884, 75473.9182555978], [64900.92049553803, 64966.37094093021, 65149.332437219884, 65321.78203645264, 65673.28030502112, 65958.46446798391, 66021.69226222567, 66282.09236872481, 66415.62169220646, 66630.43858779118  …  66444.57431842796, 66443.88109686843, 66321.12188971596, 66605.29283160764, 66948.909336649, 67445.73736601896, 67689.21514035242, 68342.4183203818, 68739.44327732305, 69084.68547805726], [64900.92049553803, 64900.92049553801, 65018.129020718785, 65124.55535208933, 65409.02948195053, 65626.88358867631, 65623.61427112174, 65816.07094984951, 65882.22138962477, 66028.72523812081  …  65711.93595097169, 65645.04949805053, 65457.754188141116, 65671.99774409624, 65944.29672656137, 66366.74084611039, 66539.22084695031, 67113.64463878442, 67435.52471016912, 67705.93879709355], [40512.94792630534, 40508.710111659704, 40454.97144334866, 40386.93714657176, 40725.337103383994, 40870.65132218625, 40988.11056470035, 41037.32601077781, 41156.87064071076, 41271.7264035852  …  41342.99332819543, 41378.08298544665, 41407.80365257146, 41484.680950719245, 41494.9448548377, 41650.27475628648, 41658.554271558016, 41887.71596908554, 42067.20324881556, 42197.198501658866], [40512.94792630534, 40467.899564900225, 40373.499688086435, 40264.99647905352, 40561.46979236468, 40665.18980011181, 40740.972629369375, 40748.79750766414, 40826.3296219906, 40899.01763463028  …  40887.13273388161, 40880.60873136005, 40868.757278684214, 40903.38407036209, 40872.28579632429, 40983.95389939704, 40950.80341960412, 41134.58893275876, 41269.23042900279, 41355.05458832212], [14866.888022051655, 14886.363484307112, 14911.968140437972, 14949.694564609808, 15049.949810535654, 15226.484237180186, 15467.740483729127, 15429.981064423544, 15573.94971447077, 15615.01318859263  …  15743.714000862876, 15867.290196312515, 16015.342879347252, 16256.662841444535, 16489.26341444541, 16649.392731116608, 16718.688756279753, 17092.947069792688, 17333.238781755583, 17256.213905299806], [14866.888022051655, 14871.366200232145, 14881.937116426077, 14904.556808117246, 14989.39304214925, 15149.938928358177, 15374.477696186133, 15321.494723514748, 15448.871468142066, 15474.00013074705  …  15570.11895504297, 15676.523302699718, 15806.855305891113, 16028.8691793461, 16241.831124340302, 16383.035841607441, 16434.64946548546, 16785.621633899635, 17004.444558251893, 16911.82574628461], [15944.236265986641, 15965.532620829375, 16041.740223826211, 16116.180080138442, 16116.369942599802, 16526.499680305365, 15956.910357406488, 16140.304796978293, 16002.30554839619, 16655.293731834514  …  16757.59649077348, 17155.55184870677, 16081.275924698544, 15453.674396319573, 15989.025574223568, 16061.580810754831, 16016.984137298246, 16673.980392235117, 16603.989791038264, 16742.548312957428], [15944.236265986641, 15949.448126562169, 16009.433966106424, 16067.520342716809, 16051.522199309631, 16443.419042511974, 15860.698118646033, 16026.824255345353, 15873.787070302624, 16504.886308537698  …  16572.82206394587, 16949.296634747705, 15871.930035517327, 15237.132470221437, 15749.099683360933, 15804.627732979381, 15744.866336593754, 16374.187836206445, 16289.02869235694, 16408.41155373751], [15944.236265986641, 15954.970620861503, 15975.025258286105, 15998.015506235588, 16052.565320900434, 16107.283611240258, 16139.918530732659, 16180.958206488063, 16233.744686608308, 16271.360551350703  …  16272.449195552466, 16249.828527892849, 16227.7563169717, 16281.04691732269, 16354.637134190003, 16428.346134272015, 16456.435958930364, 16598.361820084727, 16691.725244455378, 16769.972019438403], [15944.236265986641, 15938.896767293356, 15942.853356991522, 15949.712544247714, 15987.974309476273, 16026.310421428287, 16042.602843643373, 16067.19183562275, 16103.367463490511, 16124.420277977777  …  16093.024152417842, 16054.4625093948, 16016.503547508615, 16052.911577599807, 16109.225000352308, 16165.525546952551, 16176.852166988503, 16299.928860534817, 16375.099891871869, 16435.287955934928], [3173.2320350842083, 3171.08725190311, 3162.8377114860746, 3160.415217204934, 3165.676010968034, 3169.618334006594, 3200.973085607534, 3186.4091715792565, 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3171.4252671524455, 3173.2671849930634, 3193.723818956623, 3202.1614996396756, 3202.77083777718, 3201.0763252699744, 3200.9649847162495  …  3200.1662023559816, 3208.6994308550147, 3215.4875176274295, 3230.2298769736403, 3259.514929772169, 3256.6629071406273, 3254.494215859872, 3295.2107767314224, 3304.6561661052233, 3289.82020443711], [3173.2320350842083, 3168.0868365941064, 3157.551919939842, 3161.849752360194, 3160.498849658464, 3177.6685975275495, 3182.854057293243, 3180.2525412537434, 3175.367688702536, 3172.058325778494  …  3164.880182900939, 3170.122357177791, 3173.628333276605, 3184.966842347658, 3210.6037550620617, 3204.562832613335, 3199.202545415964, 3235.9639959286196, 3241.9701400392596, 3224.164138169377], [34195.564496956766, 34663.975210010765, 35719.990909715634, 34865.23243741235, 33198.3495904323, 33590.065022432675, 33229.75597620582, 33365.58920393154, 32865.84547223087, 32443.726780181936  …  33763.720973604664, 34996.525414017065, 35853.96980363888, 36480.80926357581, 36752.76822553279, 36414.64589482553, 35720.244742739786, 35651.177030768515, 35183.49093876889, 35308.21461531441], [34195.564496956766, 34629.05294817428, 35648.054871855864, 34759.96350599607, 33064.768761773914, 33421.2038552425, 33029.39706308768, 33130.99975953198, 32601.89172952967, 32150.740212324312  …  33391.431773664975, 34575.77439995792, 35387.223183261165, 35969.628265390005, 36201.268659956426, 35832.08459846124, 35113.38178048086, 35010.18086572228, 34516.09526453793, 34603.55650802583], [33097.63671130043, 33616.58294853476, 34576.99073555464, 33956.022018214026, 32469.452360258325, 33099.65333954545, 32603.612593946113, 32235.830084025558, 32192.984263136754, 31767.040413344515  …  32888.31581734599, 34141.135681602114, 35058.86949863021, 36042.27775674037, 35659.46198842383, 35764.44795009481, 35747.21804553951, 35476.19572809916, 34335.093135682655, 34462.01039002313], [33097.63671130043, 33582.71588324105, 34507.356571287455, 33853.4982745556, 32338.804409204233, 32933.25752897546, 32407.02901422542, 32009.183840107915, 31934.434435408904, 31480.164734580463  …  32525.679104627758, 33730.66871983305, 34602.47348607236, 35537.241605062205, 35124.36820515772, 35192.28851672461, 35139.89682498596, 34838.3456679871, 33683.7907202131, 33774.24026968802], [29576.14777688488, 29605.97434991661, 29642.861795100078, 29735.91178150527, 29750.317848058487, 29757.269023797784, 29751.2290348505, 29788.737324791193, 29877.210787216645, 29889.902434037303  …  30103.171776472966, 30226.2286169328, 30351.663739946016, 30459.080836062767, 30498.345409413007, 30622.744851569587, 30741.342853185695, 30719.625364443895, 30685.01349830784, 30812.00429991402], [34346.71094688275, 34381.34847608993, 34458.75562222355, 34593.17979543285, 34663.7523027297, 34672.652931244535, 34683.00090038461, 34758.12348233391, 34790.863397615394, 34795.23697515098  …  35195.98675066882, 35367.841000892324, 35514.96098978254, 35754.211532131296, 35680.45822923821, 35883.9842499486, 36121.58568259048, 36104.14580105458, 36019.73597621771, 36157.48815411937], [28335.32931213966, 28363.90456052843, 28427.763862229862, 28538.66103725452, 28596.881902644865, 28604.224737900407, 28612.761599370184, 28674.736182660083, 28701.74593863314, 28705.35405004546  …  29035.9643631734, 29177.740581027414, 29299.111542574487, 29496.488313702415, 29435.643357439632, 29603.548133804874, 29799.564423380783, 29785.17688021112, 29715.54051276878, 29829.183223139047], [978.0617717704336, 979.0481149238879, 983.7804094221073, 992.053562694854, 992.9034089297788, 988.5399629891984, 990.249062880964, 990.2999239383429, 989.4320668232165, 987.0201120816148  …  996.8504399760167, 1004.7389753101453, 1014.7478211769984, 1028.2038471809897, 1023.7080601887493, 1036.6644289407031, 1040.068829246164, 1045.7737182268936, 1043.7769165937937, 1039.944360168578], [0.0019383188997990075, 0.002635548588354908, 0.003560857550033214, 0.004938684593626341, 0.0035338371236599553, 0.004996719491438206, 0.00731424002310499, 0.0065051573596470735, 0.005504245885568526, 0.003221374846846592  …  0.005083254639683732, 0.006435383897546831, 0.00659924843134907, 0.003093872690568489, 0.006559175537267592, 0.008486598755058194, 0.005454964038029475, 0.00556900854096587, 0.005897914920204972, 0.004631580319042206], [2.3548476e6, 2.354996566876554e6, 2.3819910380175803e6, 2.3606198562033223e6, 2.320859257179392e6, 2.3234477472456708e6, 2.306256295458362e6, 2.321518927319665e6, 2.321894839669272e6, 2.309899850020638e6  …  2.353743041280861e6, 2.3643563766755327e6, 2.3796653343481007e6, 2.396254543235005e6, 2.3968405672501842e6, 2.3865598203849248e6, 2.3620400486280797e6, 2.357976325461291e6, 2.3503500448432197e6, 2.359044493653313e6], [0.0016459319014481277, 0.0015907812664546254, 0.0026821895480831246, 0.0017144682045708679, 1.1930843168988462e-5, 0.00023541479412231007, 0.0, 0.0008002192113992188, 0.0008832150390014211, 0.00038362127195530816  …  0.0023300112667219, 0.0027487103969864456, 0.0033335246310147696, 0.0038434218884585525, 0.0037334562135460984, 0.0032299131263133687, 0.002105412778002073, 0.0019081189442997756, 0.0015840800472062045, 0.0019369457434777156], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.6059091613054 258.50369622511636 … 137.13538556670352 505.41380923410753; … ; 516.0909801839426 261.129132748146 … 142.57915199650049 528.412016043925; 524.0475864933766 263.5380964830332 … 142.6055104785063 528.809611473538], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.2432663779743 … 136.99722836422478 504.904629508617; … ; 506.30124987310404 256.1757739721482 … 139.87456792200973 518.388567991737; 513.5889896922968 258.2785766152229 … 139.75948356387664 518.2559773122873])])

Now, apply a shock to the model and simulate it again Here, we do this by overloading the centralbankrate function with the wanted behaviour

import BeforeIT: central_bank_rate
 
 function central_bank_rate(cb::Bit.CentralBank, model::Bit.Model)
     gamma_EA = model.rotw.gamma_EA
@@ -38,7 +38,7 @@
     end
 end
 
-data_vec_shocked = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999999, 72489.76522055906, 72527.84084339692, 72358.73838111435, 72422.98511871799, 72560.02232652462, 72743.89365887582, 72853.61299495425, 73167.23957771032, 73071.23307855397  …  75395.5924001972, 75784.15786519754, 76076.90675451639, 76334.75468528284, 77138.03784370111, 77652.0506924154, 77570.27931242945, 77470.02355714855, 77729.33214994115, 78101.15737574776], [72421.99999999999, 72416.7353229158, 72381.77794214968, 72140.26494682158, 72131.57538041979, 72195.2546473543, 72305.28413433074, 72341.38800499444, 72579.61536016314, 72411.35544076416  …  74564.257346943, 74873.03137780548, 75086.53834312613, 75265.12720486247, 75980.53062957332, 76409.77363039539, 76252.41237658306, 76077.13860517836, 76254.88437191861, 76542.4658833271], [64900.92049553802, 64966.37094093033, 65006.35040679171, 64844.934239615424, 64896.67529291769, 65017.48160938105, 65179.29725993228, 65282.40309387096, 65574.0623733604, 65669.87896608586  …  67767.93601260765, 68071.18169720116, 68308.13668349921, 68576.26257428124, 69341.41456153634, 69778.33466524775, 69636.76682343057, 69526.54265275001, 69765.10238177358, 70105.79980968738], [64900.92049553802, 64900.920495538005, 64875.43493999305, 64649.147306388375, 64635.549310157774, 64690.63116044613, 64786.29849201613, 64823.41037004234, 65047.42084204213, 65076.840053501764  …  67020.70585099651, 67252.78563106398, 67418.90204320871, 67615.34961587338, 68300.90083009511, 68662.02126711285, 68453.68493524742, 68276.47881415974, 68441.72808605865, 68706.6744009948], [40512.94792630534, 40522.94571004111, 40536.530338537465, 40486.32392480483, 40567.417347960414, 40663.805326557725, 40774.395917201444, 40803.710420139716, 40926.217789377726, 39887.34217644193  …  40801.53744837455, 41306.66322834109, 41604.90956026488, 41553.25121786522, 41805.42757599962, 42154.42118913715, 42573.80982593228, 42614.17015957912, 42724.62608517559, 42861.009679024195], [40512.94792630534, 40482.12082161163, 40454.89433284793, 40364.08317783174, 40404.18546165492, 40459.383643382826, 40528.54657497727, 40516.824440144104, 40597.52920081745, 39527.135238305185  …  40351.647113572806, 40810.04762907468, 41063.29726364256, 40970.993508559804, 41178.10951915503, 41480.035000504125, 41850.50939695448, 41847.98172138678, 41914.18261093335, 42005.61785913295], [14866.888022051655, 14775.769533717319, 14903.406030000264, 14851.761343115526, 14857.757986215473, 14981.117624565872, 15020.15059527929, 15056.10636478871, 14961.577612716746, 14960.534240743113  …  15588.805239799989, 15824.674831547078, 15860.354948825547, 15967.775296359376, 16007.505697120609, 16022.391840238868, 16021.706954147161, 16071.419272054995, 16105.750184148013, 16317.884973892622], [14866.888022051655, 14760.883667645532, 14873.392249114286, 14806.919277339925, 14797.974543716326, 14905.80580226932, 14929.586552308407, 14950.24869192255, 14841.417478363088, 14825.431525503016  …  15416.918265752354, 15634.420287595613, 15653.885006699218, 15744.029620767935, 15767.302499806305, 15766.065707389293, 15749.508915957205, 15782.460562182712, 15800.240193060048, 15992.22336607881], [15944.236265986641, 16047.860335078727, 16596.732751603402, 15947.101106447082, 15836.324447835817, 16029.980800646677, 15962.976851445252, 15738.287856346566, 16144.34733316871, 17863.794312030204  …  17297.032139635983, 16982.807340129493, 16619.545142420113, 17537.72507142405, 18449.088017269318, 18102.862537999168, 17484.803565913782, 16908.329715829415, 16890.66589858587, 17157.75998257947], [15944.236265986641, 16031.692899660775, 16563.30880145221, 15898.951870795849, 15772.603528912281, 15949.396221062583, 15866.728034740847, 15627.633847467698, 16014.688082331626, 17702.47339412653  …  17106.30972898606, 16778.628973123137, 16403.192069945195, 17291.98074752082, 18172.247264366877, 17813.253046848215, 17187.748499159712, 16604.323637830017, 16570.266840538534, 16815.336702151275], [15944.236265986641, 15955.529193875194, 15969.267663238146, 15938.801934784246, 15964.249841929108, 15995.154556319909, 16027.051801450823, 16041.790424148232, 16108.157451354851, 17723.21075840783  …  16514.506938291433, 16647.327724435596, 16728.41188054685, 18532.821608285765, 18536.48069329075, 17013.360371245042, 17050.083704883935, 17045.144466852726, 17091.197059813283, 17157.989255896493], [15944.236265986641, 15939.454777572022, 15937.107357091409, 15890.677756901783, 15900.014187172006, 15914.745052320275, 15930.416644643577, 15929.002525221169, 15978.788850419374, 17563.15939542157  …  16332.412891836588, 16447.182711793124, 16510.641581963624, 18273.133667141778, 18258.328555554253, 16741.18072951482, 16760.414236535402, 16738.67850567326, 16766.994125971338, 16815.561399782346], [3173.2320350842083, 3171.6458249168054, 3174.456227649022, 3174.0199221238727, 3177.947989542563, 3186.5346910995854, 3189.247906571848, 3174.9143960837037, 3184.31913430281, 2561.581170094635  …  3219.6198408455284, 3242.330305150604, 3256.558549387151, 3169.450432737128, 3101.103545873016, 3293.8979282739037, 3327.183644265731, 3334.188921725441, 3338.575728947117, 3343.4139849074645], [3173.2320350842083, 3168.450546669597, 3168.0632304068495, 3164.4365732649208, 3165.1608199659436, 3170.51561025315, 3170.01832677347, 3152.591954851177, 3158.745078885182, 2538.4485355363586  …  3184.1193195731375, 3203.3488992062175, 3214.1647027555714, 3125.0390595079457, 3054.5694386226005, 3241.20216809188, 3270.6570292640486, 3274.241326993251, 3275.246282660222, 3276.6883292447264], [34195.564496956766, 34287.6051934052, 32958.992596245254, 32632.97183515475, 33188.06694950641, 32950.91012331123, 32563.881543828647, 32998.79164909459, 32763.392204847292, 31692.691792565118  …  32276.673231504003, 31854.489738511795, 33097.83045134575, 32850.991956319966, 32057.139753066498, 33939.95890011742, 33780.14236099916, 35150.78069501319, 35062.058675812026, 34821.689729601596], [34195.564496956766, 34253.062105976365, 32892.61689796428, 32534.44278963088, 33054.52749530515, 32785.26206220483, 32367.53752259355, 32766.781114183806, 32500.261289704365, 31406.487527026107  …  31920.78067856331, 31471.514323059626, 32666.9632254958, 32390.67314216484, 31576.10119458433, 33396.987632114084, 33206.24043489625, 34518.781487681474, 34396.96645029646, 34126.741365749374], [33097.63671130043, 33144.41555168258, 32467.820872988894, 31559.41982840696, 32026.581612799455, 32065.791548556335, 31577.5112488781, 31743.283295414643, 31628.295362399407, 31333.129443225964  …  30568.455659116575, 30184.47727333129, 31105.733348339363, 31574.988856684995, 31181.123199754427, 32567.583775076735, 32290.183394562435, 33274.67628532763, 33053.768693779726, 33057.18698934967], [33097.63671130043, 33111.02417197783, 32402.43433922839, 31464.132168775843, 31897.71564916826, 31904.593081564606, 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33510.203038096304, 34816.31557121804], [29576.14777688487, 29605.974349916596, 29673.09802471181, 29779.890270670014, 29895.452774943195, 30003.267570611508, 30083.3300632585, 30197.514192990617, 30347.170807001, 30450.63690292775  …  30727.27316714252, 30843.497437625912, 30956.226670425338, 31104.278604488918, 31262.67356179937, 31412.535203720916, 31644.371758299254, 31879.0711104245, 32007.631514243953, 32123.90166529486], [34346.71094688275, 34381.34847608994, 34502.3103521771, 34657.66284353767, 34778.587887525326, 34894.79697330229, 34932.12761183511, 35013.69188210961, 35231.6210002, 35294.45746517665  …  35497.06355982244, 35675.8553949581, 35810.60390749539, 35928.113995990665, 36128.845610603996, 36232.04644680272, 36596.04365404435, 36903.198593633424, 37022.83829547114, 37187.1103340455], [28335.329312139664, 28363.904560528434, 28463.695617623962, 28591.858224196993, 28691.61889556883, 28787.4889985212, 28818.28600665091, 28885.574850170928, 29065.361885306414, 29117.20067507227  …  29284.346531376043, 29431.84611393361, 29543.010862225517, 29639.95426563208, 29805.55371450573, 29890.69227940358, 30190.982480460716, 30444.378981120317, 30543.0792716844, 30678.600321050075], [978.0617717704338, 979.0481149238876, 980.9062609422532, 986.4322905525488, 989.3365507700739, 991.6637838779875, 984.4496084502181, 981.7935628165371, 991.5576922000151, 990.0434119945253  …  989.0028498068486, 993.0434286425964, 998.9214095480072, 1000.3072932558447, 1002.9477813378418, 1005.389619111977, 1023.158508430759, 1036.6860813222793, 1042.3601833284672, 1046.1745335371238], [0.0019383188997990075, 0.006728434425867791, 0.00908046554836961, 0.01185541336935425, 0.009637705065879798, 0.007116495451877647, 0.005715539760787092, 0.004122393759308451, 0.0032764078652021134, 0.0053481690388572645  …  0.008372601817100866, 0.0035504183401180445, 0.002995044956902726, 0.005754180390484365, 0.0022524653853615018, 0.0007814410912665437, 0.004461102266868799, 0.0026462262902717804, 0.0021137825996921933, 0.0020620202347729766], [2.3548476e6, 2.347127886802868e6, 2.346537676374904e6, 2.353541784658797e6, 2.3163016184728546e6, 2.307559769579166e6, 2.3122319577811817e6, 2.3350579501085877e6, 2.313635215710806e6, 2.344139199057313e6  …  2.3413284425446396e6, 2.3555698029072685e6, 2.366455462312521e6, 2.3728944917606143e6, 2.3661575602355613e6, 2.424216066791932e6, 2.428369956856602e6, 2.428737816441381e6, 2.407452457692039e6, 2.439073505074382e6], [0.0016459319014481277, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01  …  0.008813595575164906, 0.008840856438068942, 0.00870961552545275, 0.008464495823647581, 0.007610276652068634, 0.009478258084152001, 0.0090474659484035, 0.008453279279609474, 0.007013941251453087, 0.007833919729536409], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 518.6059091613056 258.5036962251161 … 137.13538556670352 505.41380923410753; … ; 571.2425319871543 282.1978688296847 … 146.36317814627148 535.5340908686115; 574.5730034771111 284.67965764299856 … 147.07015356300158 537.2540606751747], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 518.0834390395163 258.24326637797435 … 136.99722836422478 504.904629508617; … ; 560.4066318359042 276.8448571782292 … 143.5868148760234 525.3755441720687; 563.1060536598974 278.9982084130156 … 144.1350242403374 526.5318977549284])])

Finally, we can plot baseline and shocked simulations

Te = T + 1
+data_vec_shocked = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72422.0, 72495.70537058188, 72591.15703718243, 72633.66000303469, 72914.2301532903, 73370.8549592256, 74019.93043840158, 74476.55125681015, 75283.02508714604, 75865.68988331941  …  76854.73829190955, 77334.48143836074, 77591.68313977454, 77848.03796345774, 78344.28064890322, 78970.71878400569, 79239.68391506084, 79627.39886103489, 79818.03483082667, 80740.14298282558], [72422.0, 72422.66948852775, 72444.96662425339, 72414.3564952465, 72620.84378303321, 73002.01113001953, 73573.62704627503, 73952.91558314202, 74678.40847229543, 75180.57660247867  …  76007.31424606346, 76404.71595158777, 76581.5953845482, 76757.20586416268, 77168.67296465681, 77707.34567741284, 77893.45491647795, 78195.72502713914, 78303.96644959581, 79128.78435264973], [64900.92049553803, 64966.37094093021, 65057.27962129736, 65097.670894178766, 65357.657641483034, 65794.24861874423, 66397.25590280391, 66844.75312620249, 67575.64305309391, 68105.19269421346  …  69021.7352966818, 69447.37674951773, 69695.5718222904, 69948.94293542263, 70400.10805236147, 70984.40517826499, 71220.24463827514, 71568.36818659189, 71789.25216633735, 72604.18896831915], [64900.92049553803, 64900.92049553801, 64926.2615887978, 64901.12087074144, 65094.676795307656, 65463.493271627725, 65996.91344960508, 66374.77570721964, 67032.92633173714, 67490.16141880048  …  68260.68035750193, 68612.4351704488, 68788.27556523694, 68968.79553207337, 69343.7079257328, 69848.79707097405, 70010.26052575659, 70281.5930120546, 70427.48177136791, 71155.20235324833], [40512.94792630534, 40548.30087986238, 40542.657487082215, 40535.76176584741, 40606.0350691645, 40618.10474971421, 40795.64200456342, 40718.19030622975, 41125.46060192256, 41408.728606130564  …  41767.31434049901, 42078.59342235007, 42127.80659233436, 42137.93591221313, 42379.80343306151, 42586.07804530752, 42793.108631988645, 43013.38342115624, 42948.523876881285, 43347.19800842235], [40512.94792630534, 40507.450447337775, 40461.0091420057, 40413.37175072576, 40442.64779600217, 40413.91280865935, 40549.66455899189, 40431.905607868415, 40795.17184500131, 41034.78262403482  …  41306.775051846256, 41572.69717570313, 41579.38722737737, 41547.48541026659, 41743.86648705731, 41904.786212763094, 42066.082463615174, 42240.017262871166, 42133.83328520802, 42482.10316186421], [14866.888022051655, 14893.247315126193, 14983.292234453203, 15073.953230957792, 15272.91353373617, 15627.755457236444, 15922.579171316189, 16062.11770090132, 16196.075043450097, 16210.629347827724  …  16295.947249058445, 16217.560949751112, 16094.440836804126, 16107.939327883352, 16326.782894849834, 16211.47495473978, 16339.522346753498, 16305.231117650883, 16409.966534847666, 16526.830820979936], [14866.888022051655, 14878.243095927919, 14953.117571750568, 15028.440299079584, 15211.459621989543, 15549.19291127802, 15826.57393254503, 15949.1868833364, 16066.000354563708, 16064.2375141271  …  16116.263109139327, 16022.582873086367, 15884.923566928897, 15882.229628165562, 16081.78873227369, 15952.123871307958, 16061.9248385432, 16012.068549481313, 16098.685863504033, 16196.999209478989], [15944.236265986641, 16103.289379140766, 16057.765005085732, 15905.146611140382, 15883.022774087623, 16025.586871981226, 16176.278893929983, 16285.870060331505, 16469.65537350513, 16424.435619971966  …  16783.492171371174, 16922.026154049967, 17087.344254229894, 16887.78633345848, 16900.265866227437, 17403.076506231173, 17202.553159152743, 17353.782616641325, 18209.184141740312, 17908.167784049987], [15944.236265986641, 16087.066101668392, 16025.42647526167, 15857.124049100124, 15819.11395422345, 15945.024381188097, 16078.743971921971, 16171.36606684089, 16337.383493248495, 16276.112985710522  …  16598.432210783343, 16718.578538036072, 16864.90137759527, 16651.149163139307, 16646.666212831133, 17124.66218800663, 16910.293337201158, 17041.767445397076, 17863.77410984145, 17550.768358641442], [15944.236265986641, 15955.98639561933, 15966.262605938555, 15969.423303696884, 16017.301964124117, 16056.743748314591, 16153.940555461257, 16259.855955564299, 16379.86614233366, 16511.055698199227  …  16731.8263770668, 16849.693933962702, 16919.328877156626, 16986.674880087317, 17097.15197271608, 17187.56265218296, 17298.10134584885, 17378.122376487067, 18321.385457731987, 17652.253997343105], [15944.236265986641, 15939.911518707728, 15934.108351638695, 15921.206670420976, 15952.85284253718, 15976.024628277066, 16056.540322531902, 16145.534864115265, 16248.315381624732, 16361.950831995091  …  16547.336099460328, 16647.115942992743, 16699.07322302065, 16748.652051198744, 16840.597913231773, 16912.596123360556, 17004.21822439205, 17065.669584552037, 17973.847078959698, 17299.96193084621], [3173.2320350842083, 3172.103026660935, 3176.029647464986, 3178.533393982209, 3182.1357941785595, 3137.870539351953, 3130.920660460487, 3148.3842197776503, 3143.1095724716424, 3186.011691451787  …  3213.380532605189, 3241.7009211172235, 3263.319909289502, 3275.8969800924783, 3282.2985119247787, 3263.1989602557205, 3326.4025031821297, 3319.2378523641764, 3006.159211946776, 3407.8363112798725], [3173.2320350842083, 3168.9072878053007, 3169.6334815325467, 3168.936417554298, 3169.331774053041, 3122.096098861504, 3112.042764967113, 3126.2483090236133, 3117.866481248722, 3157.2400698388515  …  3177.9488078696186, 3202.727082037467, 3220.8380433423285, 3229.99404312256, 3233.045454513964, 3210.994321988153, 3269.889159242711, 3259.5590728275088, 2949.135374897902, 3339.8249572304317], [34195.564496956766, 34789.77179538365, 35089.7191071401, 35283.17912113073, 34767.34425844538, 35389.02924760004, 35724.904592505605, 37795.659339466074, 39082.8503075921, 38856.39209976172  …  39661.75143562509, 41085.91607525234, 40721.53385142793, 40201.94525065395, 40731.37819979593, 41022.487081357474, 40297.79202608022, 40681.36895130355, 40421.296966582, 41742.66916861531], [34195.564496956766, 34754.72279963202, 35019.05236569104, 35176.64827927508, 34627.45023619456, 35211.12447782702, 35509.50117333726, 37529.92260480299, 38768.96625970305, 38505.495267293154  …  39224.42873290171, 40591.9544419844, 40191.421330949874, 39638.62248131072, 40120.176963343, 40366.20956809471, 39613.15961055313, 39949.9316283755, 39654.54534465649, 40909.59645253491], [33097.63671130043, 33838.90399893097, 34082.27679657877, 34164.38072604154, 33615.085482143244, 34289.62136730618, 34599.47422391337, 36385.28615011837, 37591.01623932382, 37034.495790372486  …  37653.76690464424, 38969.6151630426, 38439.44239502157, 37487.56886075088, 37993.9497450315, 38252.39780363014, 37393.29224891395, 37726.36724571712, 38170.93668922443, 38784.72279924193], [33097.63671130043, 33804.812956038426, 34013.638930455505, 34061.22788293388, 33479.82782537644, 34117.243448932815, 34390.85659052097, 36129.46557970653, 37289.11348022106, 36700.051788686804  …  37238.58485860719, 38501.0970772221, 37939.03811830296, 36962.28081871914, 37423.82543084833, 37640.43616275945, 36758.00533343443, 37048.0598589859, 37446.87215361402, 38010.6828298697], [29576.14777688488, 29605.97434991661, 29625.985983422823, 29630.713454383018, 29712.792860213536, 29869.6830514688, 30096.68543646945, 30299.06063066935, 30614.12379809278, 30829.355821868416  …  31276.65537112897, 31499.283623818395, 31633.50732432486, 31774.394500204857, 32005.28452592371, 32237.614101601466, 32369.49094360816, 32539.56850284654, 32662.57081562945, 32989.95442032987], [34346.71094688275, 34381.34847608993, 34446.58506043292, 34478.49451141339, 34651.83439077018, 34926.668601593636, 35290.09051893218, 35527.777485951345, 35926.20406043019, 36226.16127843395  …  36686.98846312366, 36881.7623499412, 36987.69853148209, 37099.055058296784, 37320.277805357786, 37660.33850415938, 37763.90314810098, 37941.409912792806, 38036.82870905635, 38509.32599000129], [28335.32931213966, 28363.90456052843, 28417.723399351642, 28444.048039376114, 28587.049870675102, 28813.782435605932, 29113.598034338578, 29309.684888014533, 29638.378608121388, 29885.837136678445  …  30266.010075343886, 30426.694521459136, 30514.089689941906, 30605.956531667453, 30788.46074288592, 31069.003817346857, 31154.442516141567, 31300.881942043332, 31379.60048423146, 31769.401012013303], [978.0617717704336, 979.0481149238879, 984.7085774416197, 988.4629283823037, 993.0303904993863, 997.8969656818294, 1010.4799474023426, 1017.915009581745, 1035.3151945709362, 1049.6755939110603  …  1058.0914624290938, 1066.3307757581863, 1074.3659664833956, 1075.4933769209174, 1074.545721079943, 1086.4525725041726, 1086.8505465660114, 1087.389770952569, 1089.8526416515288, 1104.908557987957], [0.0019383188997990075, 0.003906491712359017, 0.0019052459482975515, 0.005235803914279691, 0.003051477233906752, 0.0013152179422313282, 0.007625188490721024, 0.005699138760217881, 0.006266338515272585, 0.005346656101616221  …  0.0062827513830749915, 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2.3962485794122373e6, 2.4373207588510853e6, 2.4571544529986507e6, 2.4542767427953775e6  …  2.4376108300726265e6, 2.417049804628035e6, 2.4166894322735714e6, 2.4267852803307828e6, 2.4366093938120664e6, 2.4549692381518474e6, 2.443974799136787e6, 2.4444215613176147e6, 2.4385587429177626e6, 2.439741211919264e6], [0.0016459319014481277, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01  …  0.008107434760422461, 0.0068583526013250035, 0.006481205918788769, 0.006427628548570603, 0.006485800790698189, 0.006804665619416339, 0.005984320757705683, 0.005619643519671302, 0.004997029746875778, 0.00478122505041872], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.6059091613054 258.50369622511636 … 137.13538556670352 505.41380923410753; … ; 525.6548076468496 264.68988467546524 … 143.2704084147561 529.9260524793315; 536.1394754318542 267.69618791267544 … 143.69827453898898 531.4945627600081], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.2432663779743 … 136.99722836422478 504.904629508617; … ; 515.6836610834572 259.6689819926545 … 140.55271189658714 519.8738846685097; 525.4395566702675 262.35368359298366 … 140.8304389585016 520.8873441082269])])

Finally, we can plot baseline and shocked simulations

Te = T + 1
 StatsPlots.errorline(
     1:Te,
     data_vec_baseline.real_gdp,
@@ -56,4 +56,4 @@
     titlefont = 10,
     xlabel = "quarters",
     ylabel = "GDP",
-)
Example block output

Note that, importantly, once the function central_bank_rate has been changed, the model will use the new interest rate in all the simulations, unless the function is changed again. To restore the original interest rate, you could close and restart the Julia session.

+)Example block output

Note that, importantly, once the function central_bank_rate has been changed, the model will use the new interest rate in all the simulations, unless the function is changed again. To restore the original interest rate, you could close and restart the Julia session.

diff --git a/dev/examples/scenario_analysis_via_shock-55c7bba0.svg b/dev/examples/scenario_analysis_via_shock-55c7bba0.svg deleted file mode 100644 index 3a1ea12..0000000 --- a/dev/examples/scenario_analysis_via_shock-55c7bba0.svg +++ /dev/null @@ -1,48 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/scenario_analysis_via_shock-f9751c5e.svg b/dev/examples/scenario_analysis_via_shock-f9751c5e.svg new file mode 100644 index 0000000..fe0a367 --- /dev/null +++ b/dev/examples/scenario_analysis_via_shock-f9751c5e.svg @@ -0,0 +1,52 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/scenario_analysis_via_shock.html b/dev/examples/scenario_analysis_via_shock.html index c1cbc86..6a54d0e 100644 --- a/dev/examples/scenario_analysis_via_shock.html +++ b/dev/examples/scenario_analysis_via_shock.html @@ -24,7 +24,7 @@ "Y_EA" => 2.35485e6 "D_I" => 54049.0 ⋮ => ⋮

Initialise the model and the data collector

T = 20
-model = Bit.init_model(parameters, initial_conditions, T);

Simulate the model for T quarters

data_vec_baseline = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72422.0, 72474.35204292928, 72500.67749532155, 72479.97217810026, 72524.38479605025, 72944.98676292054, 73296.87194582711, 73719.31241025148, 73994.37474483742, 74224.6377515799  …  74223.36403833711, 74244.72559165639, 74243.3548214, 74652.6570142359, 74660.63317396006, 75053.3091144611, 75090.68027577705, 75122.74458543962, 75418.10648195737, 75787.19722139236], [72422.0, 72401.33767330972, 72354.66929799739, 72261.1327014391, 72232.5670539482, 72578.28382271383, 72854.92823704587, 73201.00079183122, 73400.10759996096, 73554.34416870477  …  73404.95433650722, 73352.10716134965, 73276.85557073976, 73606.59963502412, 73540.30105343927, 73852.60670531333, 73814.94006183329, 73772.06291943214, 73987.500345904, 74274.68622267427], [64900.920495538005, 64966.370940930225, 65009.76162902033, 64965.39434724502, 64997.12981187437, 65402.09094537466, 65731.2647164538, 66107.98959866511, 66346.07146921755, 66551.99108685428  …  66514.29513029265, 66521.27112465473, 66507.32551657017, 66891.63658794035, 66881.61132696056, 67255.34297325698, 67265.83763517895, 67287.65194572217, 67569.3800715236, 67907.71197340774], [64900.920495538005, 64900.920495538005, 64878.839292404366, 64769.24370765724, 64735.599628362244, 65073.30702053437, 65334.937859106605, 65643.19227542488, 65813.22974169781, 65950.98616578775  …  65780.88799966338, 65721.50909253575, 65641.5338180429, 65954.32915831967, 65878.00856796447, 66179.39238175281, 66123.0362411757, 66077.84260857242, 66287.65643447141, 66552.45455496124], [40512.94792630534, 40440.97467769727, 40333.81893759827, 40506.55871060514, 40564.61834049748, 40599.27996257108, 40699.01242453883, 40948.69124671697, 41136.25261834324, 41266.448275478106  …  41602.790825908676, 41667.16935802959, 41731.68524443336, 41843.421470098074, 41947.6684866807, 41997.274789030445, 42201.00923150865, 42242.32365512629, 42261.84527548088, 42419.06364196005], [40512.94792630534, 40400.232371078906, 40252.591169834915, 40384.25686854891, 40401.397716611464, 40395.182655912635, 40453.61760730934, 40660.78592396843, 40805.877188066006, 40893.78717122756  …  41144.06562426542, 41166.2195193535, 41188.42258322197, 41257.09780057458, 41318.21601100873, 41325.40262760551, 41484.0424341921, 41482.82089194329, 41460.18029087267, 41572.49189018559], [14866.888022051655, 14878.991589799392, 14858.77668598505, 14786.833104116473, 14982.343426806747, 15020.088808085406, 15082.61737108209, 15217.330025565983, 15328.202307959258, 15286.890659169087  …  15081.951186318534, 15030.772789962499, 15311.821172150609, 15173.428894544937, 15449.505496387625, 15347.354934566014, 15344.79309658451, 15520.853741589612, 15574.705139490825, 15688.736864173454], [14866.888022051655, 14864.001732548053, 14828.852783570454, 14742.187076796994, 14922.058687508299, 14944.581073112582, 14991.676684499533, 15110.3388085333, 15205.09771990034, 15148.840747154027  …  14915.652941374608, 14850.06305324846, 15112.49203724383, 14960.813859824108, 15217.675460692153, 15101.828038265201, 15084.095370101477, 15241.794014668876, 15279.268542377275, 15375.631380225572], [15944.236265986641, 16167.970452874444, 16341.576245128988, 16087.22330350771, 15549.423926068755, 15864.523803144848, 16071.17054003792, 16264.221251938488, 16244.473360819566, 16424.371476549586  …  16556.37011561356, 16782.917115888496, 15663.725071626024, 16687.02984982915, 16061.434198153707, 16873.207686033482, 16716.930617068727, 16246.222077341868, 16581.046236870356, 16314.177241465039], [15944.236265986641, 16151.682012379677, 16308.66615143863, 16038.650995560112, 15486.857414214073, 15784.770995148603, 15974.269369164316, 16149.869468615776, 16114.009973062788, 16276.049421542257  …  16373.814472855209, 16581.141959302342, 15459.814855274757, 16453.20574483832, 15820.421768056895, 16603.27020615803, 16432.921195878527, 15954.120471858138, 16266.520354490562, 15988.589630134456], [15944.236265986641, 15949.97169688411, 15965.760506829172, 15993.420422586925, 16005.187879800953, 16050.574126034118, 16118.649008522638, 16201.440332380795, 16271.335715364421, 16327.914191636353  …  16378.491399657665, 16385.262917577584, 16364.102454569871, 16446.531351295827, 16462.20278302087, 16507.093463149737, 16536.26871308014, 16550.88650615304, 16580.376421372628, 16639.953478746305], [15944.236265986641, 15933.902879487692, 15933.607263700364, 15945.131334579306, 15940.787501884757, 15969.886021406022, 16021.461566081198, 16087.529954152053, 16140.656589387067, 16180.463204525848  …  16197.897102477938, 16188.268618668157, 16151.074732450943, 16216.077189708782, 16215.176555566137, 16243.013076514097, 16255.328616274797, 16253.30713677036, 16265.863244723669, 16307.864239699242], [3173.2320350842083, 3166.0883279257173, 3158.3126844564517, 3169.9704633264723, 3177.040515998382, 3159.2541940342876, 3157.6985155600396, 3171.3967538551883, 3196.0910859416445, 3213.491805335185  …  3255.836671199018, 3264.4188078712277, 3262.4327555330897, 3281.678748851086, 3288.5436083875898, 3269.780849714221, 3299.2550240580645, 3307.1018439306517, 3294.659570963819, 3300.1000372092203], [3173.2320350842083, 3162.898648585268, 3151.95219849171, 3160.399341037398, 3164.256997840275, 3143.3722554224155, 3138.659162909199, 3149.099044742066, 3170.422487065618, 3184.472021595084  …  3219.936811986131, 3225.1718395657344, 3219.9624385446177, 3235.6947958514816, 3239.196839178901, 3217.470914416769, 3243.202897551968, 3247.6412657413593, 3232.1631703208855, 3234.2388128052094], [34195.564496956766, 32576.025318769858, 30947.33890374104, 31780.75423404153, 33188.19621124658, 33004.78288216836, 33507.43764681084, 33574.98879120878, 33876.71347012681, 34408.90264930789  …  33441.89627679147, 35063.34554303561, 36045.87563114741, 34774.982222564875, 35657.28464720056, 36329.3853078553, 35979.53013541196, 36977.02025036349, 37154.34311339021, 37093.33411231671], [34195.564496956766, 32543.206564461332, 30885.014449385355, 31684.79829731215, 33054.65623693248, 32838.863996431784, 33305.40445122867, 33338.92708349822, 33604.641196239594, 34098.16934917649  …  33073.15561520705, 34641.79117387993, 35576.63078262589, 34287.70382925952, 35122.22353637751, 35748.188009872436, 35368.26208851223, 36312.18587042481, 36449.56234231951, 36353.049765079115], [33097.63671130043, 31589.609996211562, 29980.83327713186, 30681.397174170786, 31760.19710856951, 31543.688693049186, 32063.36603664231, 32285.918905178743, 32591.267012411627, 33161.97530892472  …  32459.64436629527, 34299.47921525989, 34509.75229795772, 33826.205422800915, 34455.25965446269, 35493.913603024506, 35151.582804796846, 35863.67513898165, 36153.83328327487, 35728.114638523024], [33097.63671130043, 31557.785007158323, 29920.455256231977, 30588.76053677908, 31632.403001318464, 31385.11489789175, 31870.03987515589, 32058.920492784462, 32329.51847730798, 32862.50252039551  …  32101.73431719529, 33887.10854443468, 34060.50468762704, 33352.22159947221, 33938.235722696176, 34926.08217658815, 34554.38102685108, 35218.85832946296, 35468.03118415593, 35015.076442950674], [29576.147776884885, 29605.97434991661, 29629.908616463137, 29640.26839050235, 29667.018326189114, 29809.91627391501, 29968.254406745287, 30128.231937641343, 30224.37898023337, 30320.131235907873  …  30367.019683611663, 30365.716103214396, 30353.961977981508, 30481.39066276418, 30514.517290105818, 30633.979264991423, 30664.4249726064, 30675.905999122897, 30773.3841021436, 30898.472671998974], [34346.71094688275, 34381.34847608993, 34406.160942460374, 34361.42818850695, 34365.85512817582, 34617.16521740784, 34784.38437491573, 34994.83431269168, 35131.41347463892, 35236.72757841601  …  35153.88550497037, 35161.631757794385, 35152.56129968366, 35395.84469351625, 35357.67287902721, 35602.0848637505, 35578.49053568161, 35589.498699432486, 35765.675732366515, 35971.02253803006], [28335.32931213966, 28363.90456052843, 28384.374334380722, 28347.470733442944, 28351.12286751646, 28558.448516509896, 28696.400878312812, 28870.01774368962, 28982.692740050577, 29069.57470718716  …  29001.23170236167, 29007.62220144157, 29000.139260266293, 29200.843050829608, 29169.352089856322, 29370.986944666092, 29351.522109830392, 29360.60361826028, 29505.94604284182, 29675.352929302397], [978.0617717704336, 979.0481149238879, 973.692070096811, 963.6977682355973, 964.2563575095813, 975.0094540518211, 978.6259347929579, 984.9233483320202, 990.2790143452612, 995.1322725304058  …  993.3899417106869, 993.9232636459581, 1000.8022389050561, 1014.4012316599171, 1009.4211578275372, 1019.2788445150399, 1022.9221268909012, 1022.2472471667751, 1030.3202370134668, 1038.2167633786967], [0.0019383188997990075, 0.0028540605204823155, 0.001001084636563121, 0.004376684203776682, 0.0015725718980503789, 0.004313687601069649, 0.002279154218084445, 0.0030138217672484036, 0.0005527697217253547, 0.007966899899319424  …  0.0002577005024497758, 0.0024651948289349246, 0.004850400160717028, 0.008718739937380926, 0.005249933894092118, 0.005823268137937765, 0.005765342364033588, 0.005556423169326141, 0.005709202712355976, 0.007185963396163286], [2.3548476e6, 2.3182728193180757e6, 2.283460755984142e6, 2.304703905170909e6, 2.321818044823849e6, 2.3263389258442614e6, 2.3419420521651534e6, 2.3302927829362964e6, 2.3398182131665577e6, 2.3494056042543813e6  …  2.3212440057627345e6, 2.357895597789562e6, 2.3654023159422404e6, 2.338521372302773e6, 2.3472208382134833e6, 2.3598376968040806e6, 2.34904200826309e6, 2.3635173660507393e6, 2.343158631383005e6, 2.334266614630387e6], [0.0016459319014481277, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01  …  0.0075177030845669434, 0.008570782451205263, 0.008360711382528814, 0.006824036119240345, 0.006805212868729497, 0.006963741638510874, 0.006130524292491828, 0.006410288654342015, 0.005219482557589373, 0.004624707024909909], [518.0834390395164 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258.2432663779743 … 136.9972283642248 504.904629508617; … ; 553.7767890179489 278.9529185921488 … 143.8601561293128 526.5061856021143; 553.8089455860217 279.3241067901476 … 144.38640074200407 527.8163168963247])])

Now, apply a shock to the model and simulate it again. A shock is simply a function that takes the model and changes some of its parameters for a specific time period.

In this case, let's define an interest rate shock that sets the interest rate for a number of epochs.

We do this by first defining a "struct" with some useful attributes

struct CustomShock
+model = Bit.init_model(parameters, initial_conditions, T);

Simulate the model for T quarters

data_vec_baseline = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72422.0, 72482.17886654705, 72839.0439535848, 73050.97018237105, 73291.65320300258, 73601.43782920495, 73993.22299523717, 73985.40928890712, 74088.8102754535, 74065.59371855546  …  74198.85585289636, 74236.87303221022, 74432.82871668038, 74704.63685879551, 74929.71415460567, 74862.89693275974, 75166.5291784729, 75283.84751195589, 75362.52762386456, 75686.7775369771], [72422.0, 72409.15661178499, 72692.35432433043, 72830.40668594724, 72996.74818846468, 73231.43483306852, 73547.08063571894, 73465.22677533976, 73493.78469544792, 73396.73639994458  …  73380.71638587752, 73344.34901048626, 73463.86289674653, 73657.85112104836, 73805.34429622, 73665.24073660452, 73889.50034262643, 73930.26926954759, 73932.97576322588, 74176.27064822474], [64900.92049553803, 64966.370940930254, 65293.83317658212, 65484.50615310138, 65709.21905691604, 65989.10951683734, 66337.69984323585, 66304.65416274894, 66390.03732880189, 66355.9397859308  …  66463.70374835031, 66492.12771646437, 66668.48956091437, 66919.67087204198, 67121.53587777383, 67040.16224245242, 67318.4356354505, 67421.38594211277, 67486.50618702403, 67800.17534998983], [64900.92049553803, 64900.920495538005, 65162.33875186982, 65286.78815424857, 65444.82362640801, 65657.37458130164, 65937.71648347631, 65838.4741146139, 65856.8425005776, 65756.70532724576  …  65730.85445390055, 65692.71606537378, 65800.59983046986, 65981.97061688922, 66114.33289240277, 65967.65411104736, 66174.74063675731, 66209.1721127151, 66206.3545847692, 66447.0640766932], [40512.94792630534, 40453.79241725306, 40552.09958166174, 40633.202552324845, 40672.930775976136, 40812.3506325723, 41031.36813458389, 41202.11979355534, 41300.65335256123, 41370.19874170262  …  41528.4365669332, 41579.52659502329, 41679.322970076166, 41787.32593609307, 41900.79374361453, 42010.00683784348, 42120.49660582642, 42221.83800771901, 42289.15554275522, 42307.43001942751], 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15802.338977068632, 15839.344302526266, 15976.97067014789, 16003.7978681967, 15844.158273785486, 16026.568039389422, 16040.612369744948, 16032.803643822705, 16074.360539043559, 16077.996933384387], [15944.236265986641, 15956.927445049878, 16087.118231046683, 16067.791150462303, 15969.742966729815, 16178.179309404464, 16547.34462015482, 16405.54545995725, 16606.098794319674, 16618.31692382491  …  16259.12000482161, 16360.527006955057, 16002.434721195817, 16087.637882176063, 16897.874943280498, 16271.872096242478, 16713.76548936121, 16411.888166807927, 15851.736887991927, 16616.52980435066], [15944.236265986641, 15940.851620076943, 16054.720587132162, 16019.277514201145, 15905.485209182356, 16096.849718664647, 16447.572356239896, 16290.20004312672, 16472.73110316655, 16468.243423574142  …  16079.841933454687, 16163.83009926829, 15794.115186013532, 15862.212652937164, 16644.311167262433, 16011.554779686954, 16429.80984156469, 16116.807940788654, 15551.045275368677, 16284.908039579814], [15944.236265986641, 15954.251914177627, 15998.590693281947, 16036.16592245556, 16069.58157757529, 16135.361579054857, 16198.195167410267, 16233.933783176342, 16250.288412966358, 16248.423387286575  …  16267.093263228597, 16290.503501241079, 16324.41329387179, 16349.109526009188, 16420.572375915865, 16416.22932124981, 16471.669463627844, 16490.73572225061, 16521.293432466224, 16562.386324773164], [15944.236265986641, 15938.178784671356, 15966.37133385592, 15987.747772549299, 16004.92209751652, 16054.24723803005, 16100.5281011657, 16119.794947403394, 16119.778323085948, 16101.690250443422  …  16087.727276255584, 16094.648461730898, 16111.902244850071, 16120.020470813844, 16174.170840220346, 16153.602578635808, 16191.826864674795, 16194.237843719657, 16207.90100109213, 16231.845119930278], [3173.2320350842083, 3170.368545219235, 3162.525474185151, 3169.0110263590927, 3168.2934356007627, 3180.6152661072383, 3182.5707255789484, 3228.8276163141927, 3231.5964824701196, 3240.677902439034  …  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28654.094283340535, 28776.209625783376, 28903.691594696415, 29081.982100355403, 29059.279688991315, 29102.20847400741, 29092.692520810822  …  29136.495493541766, 29144.49440016298, 29220.767398077405, 29351.423896100856, 29444.245662732916, 29371.84802880201, 29520.722382967433, 29557.56052421475, 29586.491809552084, 29734.948883319743], [978.0617717704339, 979.048114923888, 986.113407593733, 992.1786570447834, 999.5315502219956, 1004.4094449129501, 1009.930967596549, 1016.4165723235664, 1020.9754581000396, 1024.5762588480177  …  1034.881397883193, 1033.3227090107057, 1034.4600328971308, 1039.8418108384253, 1040.9920067761202, 1032.1774338206121, 1049.3818955705717, 1033.486370864788, 1042.49278255596, 1051.7159242310156], [0.0019383188997990075, 0.0028757694938645884, 0.004840000455283988, 0.002607260886745788, 0.002981527532834205, 0.006953111022180769, 0.004830867005468642, 0.0017027529907025496, 0.0015998445659046734, 0.0009756531941349511  …  -0.0002630685930093968, 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504.90462950861706; 518.6059091613041 258.50369622511613 … 137.13538556670352 505.4138092341076; … ; 510.6767089583184 260.9976960017441 … 142.21792619459336 528.3770975671101; 505.32893740795964 256.9357957088257 … 142.32411548068052 529.647632916932], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.2432663779743 … 136.99722836422478 504.904629508617; … ; 500.9896819636838 256.04683044951753 … 139.52019421269418 518.3543118835423; 495.24391508463845 251.80804021420659 … 139.48370446220207 519.0772740753025]), BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72422.0, 72492.86532709548, 72411.64734727719, 72574.97842370647, 72866.35455154335, 73046.54570894543, 72952.09552203688, 73011.55947619377, 73172.70223014345, 73323.64947363971  …  74348.6001350424, 74881.55983894745, 75445.2838746264, 75755.02932713756, 75863.41512367484, 75869.41922859983, 76182.90535750276, 76357.93948247566, 76481.25287306341, 76947.42008306239], [72422.0, 72419.83230624639, 72265.81844664193, 72355.85209377471, 72573.16081925246, 72679.3322200944, 72512.23064373477, 72498.22398352266, 72585.0341406821, 72661.49236241679  …  73528.80954138847, 73981.28497269012, 74463.1379235704, 74693.52514752168, 74725.03446545849, 74655.66069452296, 74888.608973149, 74985.04942790957, 75030.47991207172, 75411.75412013476], [64900.92049553803, 64966.370940930254, 64898.169104874, 65055.19017075315, 65312.06478312069, 65473.53455451669, 65370.6542661932, 65414.05454331101, 65557.02629832865, 65721.85042656456  …  66679.00340739616, 67177.0629946147, 67679.52385004294, 67952.55632119424, 68033.37701479948, 68029.66575787538, 68315.50284696142, 68463.12769464342, 68691.75544217828, 69046.55952228226], [64900.92049553803, 64900.920495538005, 64767.47150303079, 64858.76840977229, 65049.26738979366, 65144.391474336415, 64976.501710635115, 64954.13619397682, 65030.52158794726, 65128.34218011096  …  65943.78015250883, 66369.41660566245, 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146.43802109527303 535.8076977314864], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.2432663779743 … 136.99722836422478 504.904629508617; … ; 519.1076489587603 267.74027277235496 … 142.80842815862906 523.0165030600481; 524.1127646037661 270.79622893728566 … 143.51550745632798 525.1144003708694])])

Now, apply a shock to the model and simulate it again. A shock is simply a function that takes the model and changes some of its parameters for a specific time period.

In this case, let's define an interest rate shock that sets the interest rate for a number of epochs.

We do this by first defining a "struct" with some useful attributes

struct CustomShock
     rate::Float64    # target rate for the first 10 epochs
     final_time::Int  # number of epochs for the shock
 end

and then by making the struct a callable function that changes the interest rate in the model, this is done in Julia using the syntax below

function (s::CustomShock)(model::Bit.Model)
@@ -32,7 +32,7 @@
         model.cb.r_bar = s.rate
     end
 end

Now we define a specific shock with a rate of 0.01 for the first 10 epochs, and run a shocked simulation

custom_shock = CustomShock(0.0, 10)
-data_vec_shocked = Bit.run_n_sims(model, 4; shock = custom_shock)
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41351.42052287345], [14866.888022051655, 14710.633616338231, 14702.583132922538, 14729.338084027991, 14677.191018675243, 14950.487227792604, 14987.365454862878, 15168.908385380175, 15501.716487237869, 15738.419498428288  …  15733.437505225531, 15655.432694518675, 15632.846234513703, 15667.700043977557, 15892.182987819795, 15780.424525002705, 15809.899033257356, 15817.91069446376, 15815.690173350615, 16022.39477695487], [14866.888022051655, 14695.813371521584, 14672.973786728713, 14684.865651975291, 14618.134126906807, 14875.329388066457, 14896.999089994313, 15062.257615075392, 15377.218363842432, 15596.292006614913  …  15559.955771215828, 15467.212889728728, 15429.33799855534, 15448.159120696766, 15653.710283939141, 15527.969383875828, 15541.299469356392, 15533.50998350145, 15515.68233089293, 15702.63036799895], [15944.236265986641, 16414.244302179748, 16264.714412360883, 15736.369282579799, 16165.546747811182, 15999.714502575014, 16213.599082644834, 15748.540587013707, 15617.989739588791, 15741.808388304682  …  16313.22948368928, 16283.204421191822, 16400.771514163665, 16302.544241077647, 16376.835640603973, 16395.41747925414, 16526.129817153105, 16533.671912941885, 16744.08409756225, 16621.41009174018], [15944.236265986641, 16397.707752811235, 16231.959109743288, 15688.856311549924, 16100.501130877507, 15919.282075194777, 16115.83913844732, 15637.814492418778, 15492.557796914005, 15599.650292756407  …  16133.354784521429, 16087.437135974307, 16187.266434593777, 16074.107673842758, 16131.0904035137, 16133.123684369162, 16245.361973378735, 16236.402056152016, 16426.465549842855, 16289.690929405986], [15944.236265986641, 15960.296579453752, 15932.819048566264, 15913.384741194568, 15943.226313488869, 15945.45114858631, 15947.050396215765, 15970.042606920735, 16045.90881122171, 16121.856629034188  …  16248.352558090091, 16285.793651618535, 16334.036054334729, 16377.975355271092, 16398.64245846233, 16444.834785914267, 16517.195661700913, 16593.121535242037, 16668.280256359827, 16741.788981532227], [15944.236265986641, 15944.217360242474, 15900.732145823567, 15865.33730568906, 15879.075251501774, 15865.291509403536, 15850.897608120878, 15857.759158266635, 15917.040144528182, 15976.26646692876  …  16069.193211911466, 16089.995236987033, 16121.399736313568, 16148.48182266109, 16152.56999566482, 16181.750413240505, 16236.579603225944, 16294.78279425025, 16352.099631778776, 16407.667376549412], [3173.2320350842083, 3176.4132104953596, 3177.335989511194, 3176.861123723722, 3184.2983091492765, 3187.6007566812573, 3191.144164220242, 3192.9489479695267, 3198.0539542124566, 3192.844756389898  …  3204.8109037410413, 3214.1055060402637, 3221.8964886667995, 3238.389812723393, 3229.159035703299, 3243.1010703496268, 3241.5494927276095, 3250.2279835772492, 3260.236169008886, 3287.535726904672], [3173.2320350842083, 3173.213129340048, 3170.9371927530356, 3167.2691963974776, 3171.4855876712654, 3171.5763165990465, 3171.9031509307083, 3170.4997079719383, 3172.3695910553447, 3164.0114280483535  …  3169.4736703761423, 3175.4634370073854, 3179.9538723950814, 3193.0124384161995, 3180.7033712363714, 3191.217958009614, 3186.477744433799, 3191.789978256957, 3198.392745911555, 3221.925252735906], [34195.564496956766, 34465.99272237795, 35262.01843278985, 36192.63254971931, 36093.84865392906, 35785.33764380507, 34796.297569824805, 34283.37706758425, 34143.958721784074, 35446.852429035986  …  36310.75315928286, 35671.004758242845, 35093.29118627509, 36070.91596259129, 36808.13263108753, 37677.40409713934, 38197.43048854454, 37756.251863760306, 37672.1863287131, 36786.25581618433], [34195.564496956766, 34431.26991822715, 35191.00469990834, 36083.355786385255, 35948.617150792736, 35605.440585588854, 34586.49318947923, 34042.33476710918, 33869.74013510996, 35126.74581166435  …  35910.37959697783, 35242.1447143676, 34636.44707253179, 35565.478523032005, 36255.802286032966, 37074.6412022903, 37548.481804572664, 37077.40716165411, 36957.58258918205, 36052.09993546156], [33097.63671130043, 33649.620030465245, 34445.95305578486, 34922.75105100759, 35119.922791510835, 34951.46442356405, 34174.0530279341, 33310.9995816467, 33071.3715983404, 34307.58701407485  …  35393.0406664035, 34557.48583588952, 34053.2932085399, 34949.67708200191, 35861.96243533898, 36358.439330158566, 36839.56776637499, 36168.24377134747, 36130.111732772784, 35226.04351817794], [33097.63671130043, 33615.71968192523, 34376.58278664343, 34817.308453088444, 34978.61009233188, 34775.75934309975, 33968.00047291629, 33076.79395031535, 32805.767224431474, 33997.7686556961  …  35002.78608506078, 34142.01324148849, 33609.98777807117, 34459.95080734698, 35323.82999909814, 35776.77722609382, 36213.68825787633, 35517.95092031819, 35444.759607769236, 34523.02532756096], [29576.147776884885, 29605.97434991661, 29563.24311325228, 29537.92787075053, 29595.964354160835, 29604.875692169273, 29575.560547597222, 29596.54861242116, 29713.849391836357, 29874.5205808665  …  30168.592266972384, 30245.81008557569, 30338.588467894722, 30396.71823027157, 30478.236002690825, 30555.218859610326, 30744.50396483407, 30912.203704695796, 31053.802168045197, 31164.475415074365], [34346.71094688275, 34381.34847608993, 34287.979105266, 34268.14610045285, 34338.590320835036, 34335.6382106215, 34431.89933282723, 34481.940933560385, 34711.284233565966, 34944.24421578349  …  35273.92484344359, 35352.50425326331, 35407.827313533184, 35448.211856442984, 35550.65413178743, 35750.52860632675, 35877.994296479024, 36060.94306779688, 36211.27934902259, 36324.948763017295], [28335.32931213966, 28363.90456052843, 28286.876752129112, 28270.51493153152, 28328.629962858773, 28326.194532718306, 28405.607976464747, 28446.89126668352, 28636.094766874292, 28828.281955420298  …  29100.261684921286, 29165.08808002616, 29210.728465614353, 29244.044881973383, 29328.557649785675, 29493.449975747284, 29598.606545529154, 29749.535514843268, 29873.55985134177, 29967.33477736712], [978.0617717704336, 979.0481149238879, 979.9428901849948, 986.1303685577066, 992.5912515043683, 991.6240157749926, 995.2997626494996, 993.664490300186, 992.6597998924836, 998.349681632556  …  1015.7045571409152, 1019.4711729281863, 1017.0352347162537, 1013.80430558097, 1018.919169771012, 1029.3965763817207, 1034.86109659735, 1037.3554245491675, 1041.9638281013008, 1042.9966233796792], [0.0019383188997990075, 0.0059337629210785, 0.003943337011621528, 0.004535312137140979, 0.00623670731643422, 0.0057231191671611725, 0.00904737445662529, 0.006121017831568709, 0.007029923195770005, 0.0019599562491985356  …  0.0020302158278751037, 0.00401685895213677, 0.005091218389326357, 0.007001375159919165, 0.006856659645657626, 0.008684781054133506, 0.005709028050161713, 0.008313107337827086, 0.00784404480625156, 0.0024787722012944435], [2.3548476e6, 2.34915898809926e6, 2.3567542943364447e6, 2.3702216865586527e6, 2.380602603509879e6, 2.3697841881565345e6, 2.3564159198731207e6, 2.3465596353763165e6, 2.359921057995423e6, 2.3950357205390623e6  …  2.4185775943704867e6, 2.4129792043594285e6, 2.396628844506011e6, 2.415444703729562e6, 2.417570320801106e6, 2.4357073661518255e6, 2.4409909022970395e6, 2.425077891673508e6, 2.412809984563572e6, 2.382180876097404e6], [0.0016459319014481277, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0001453196167151053, 0.0, 0.0, 0.0009328885408478772, 0.001107728512764879, 0.0019629323123390805, 0.0021619835756051483, 0.0015514785613892816, 0.001119043065576464, 0.0], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.6059091613057 258.50369622511613 … 137.13538556670355 505.4138092341076; … ; 530.1915929097995 268.8320363764925 … 143.19514965207782 525.8481628264552; 536.5777766986058 271.4109900780944 … 143.75991903588113 527.7953497136106], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.2432663779743 … 136.9972283642248 504.904629508617; … ; 520.1343880622946 263.7325612139914 … 140.47888071745302 515.8733485084738; 525.8691106088339 265.99434818188473 … 140.8908531951007 517.2619575209776])])

Finally, we can plot baseline and shocked simulations

Te = T + 1
+data_vec_shocked = Bit.run_n_sims(model, 4; shock = custom_shock)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72422.0, 72494.22429199198, 72639.65579159373, 72343.74956504868, 72274.86918974877, 72037.16618255888, 72189.34543105954, 72105.9574369717, 72124.23892499406, 72305.64880083727  …  72594.30539324938, 72781.9948010103, 72800.28578589183, 72711.78282043048, 72893.43006417378, 73113.74250919904, 73073.24648886012, 73149.05656704007, 73303.94916740926, 73853.15880066802], [72422.0, 72421.18990205211, 72493.36770763666, 72125.3213866324, 71984.0554283224, 71675.02696209207, 71754.07955671418, 71598.98912331036, 71544.99130394014, 71652.6848542968  …  71793.85819442988, 71906.96227262268, 71852.57239350557, 71692.92160346854, 71799.6159937039, 71944.06927548774, 71831.78111969684, 71833.86114738243, 71913.44648353472, 72379.2460677583], [64900.92049553803, 64966.370940930254, 65099.2300619872, 64822.134430350314, 64746.67986034948, 64514.85463828205, 64661.55137358504, 64574.454185807364, 64581.819407386894, 64752.34652541611  …  65009.74763400128, 65179.77926367338, 65191.76637306494, 65098.19903324595, 65263.10506174453, 65473.63356913696, 65425.57348966248, 65487.23618648827, 65630.59115590756, 66167.35126715523], [64900.92049553803, 64900.920495538005, 64968.12754602578, 64626.41633680656, 64486.157417055634, 64190.53095364365, 64271.67435604055, 64120.43896559759, 64063.14682501183, 64167.59348832696  …  64292.930108057466, 64396.14552012495, 64343.100610312664, 64186.02183559913, 64283.78905841963, 64426.18675446998, 64314.03694725025, 64309.79771878128, 64385.64440770239, 64846.826822171206], [40512.94792630534, 40547.878858604716, 40606.391984738504, 40613.38624010631, 40662.82264372573, 40649.93565363885, 40668.33224467279, 40713.82823546347, 40755.564187109616, 40815.38126628746  …  40964.038159167925, 41063.822057523364, 41124.26159094874, 41172.15200550473, 41213.093210075785, 41302.282090495806, 41377.82941214415, 41490.35118494688, 41499.280838636805, 41486.66139526196], [40512.94792630534, 40507.02885124591, 40524.615285563355, 40490.761852663396, 40499.20687332295, 40445.58369492457, 40423.12241367971, 40427.5742062972, 40428.246159883725, 40446.79357122922  …  40512.35604141472, 40570.126052921005, 40588.90636478428, 40595.23438266398, 40594.66352167895, 40641.528418279355, 40674.84788696789, 40744.36863371033, 40712.08093958092, 40658.697913815835], [14866.888022051655, 14902.05541785343, 14476.464686311781, 14655.879951271934, 14415.650723252658, 14735.36648995271, 14536.074691378337, 14626.709680822614, 14622.187872827179, 14663.434135499328  …  14769.00669643684, 14710.115611975272, 14730.787890641524, 14891.010530690988, 14750.257881463203, 14555.525704164704, 14489.978807790732, 14495.080260637518, 14965.407873852491, 14772.967925434414], [14866.888022051655, 14887.042324921897, 14447.310717197346, 14611.62931206553, 14357.64619612951, 14661.2900872186, 14448.42938551894, 14523.871044392587, 14504.753461508833, 14531.01441474797  …  14606.159073948103, 14533.261024628351, 14539.022641183263, 14682.352834199566, 14528.920518142546, 14322.666487354465, 14243.80380183251, 14234.463595692729, 14681.529037170123, 14478.138755203054], [15944.236265986641, 16061.711603414118, 16583.77452727122, 16338.710583309317, 16471.52453522221, 15823.555241134922, 16137.41695536143, 16192.379242102686, 15536.77943821899, 15928.605820376099  …  15915.527924434024, 16048.118523966894, 16523.20329468072, 15847.587955415882, 15599.283928017128, 16344.993882636585, 16092.92330703401, 15925.918755245404, 15455.28469715534, 16251.008887522994], [15944.236265986641, 16045.530213519889, 16550.376673524093, 16289.378957400548, 16405.247749664264, 15744.008386869235, 16040.116351528392, 16078.532571309806, 15411.999715582482, 15784.760830505258  …  15740.038405306612, 15855.177594465264, 16308.104399416112, 15625.526384046563, 15365.206368013476, 16083.506764161031, 15819.515350849535, 15639.576095698696, 15162.113386529794, 15926.681948622527], [15944.236265986641, 15955.363901602377, 15981.03885560566, 15942.607327627647, 15933.605001830649, 15894.898772696284, 15909.928895558853, 15912.491516262962, 15915.534190801896, 15954.038236353896  …  16003.061347550276, 16038.696391901116, 16043.584819287542, 16036.549528047948, 16070.228412821385, 16119.671753286573, 16124.953711445492, 16139.556215735995, 16184.531023587893, 16262.35416155696], [15944.236265986641, 15939.289651823092, 15948.854843603509, 15894.471660086027, 15869.492653297692, 15814.993266192121, 15813.999931663284, 15800.612702404893, 15787.712594966219, 15809.963576312295  …  15826.606657904917, 15845.868741405744, 15834.729592539607, 15811.840165490144, 15829.084019724056, 15861.789336945236, 15851.001580207248, 15849.372426546732, 15877.5266386788, 15937.800801144824], [3173.2320350842083, 3171.4805326439855, 3178.6462614622606, 3181.1036106792367, 3184.9725191101725, 3183.961786115381, 3186.716761843251, 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3172.840425340097, 3175.08619898729, 3175.6522367330094, 3177.048962616567, 3175.531463499173  …  3161.905732438172, 3154.7142115298593, 3174.712953850757, 3188.148304519382, 3179.706581853169, 3175.382030543359, 3196.948986157712, 3211.189443719711, 3210.6783938458857, 3220.5827995607187], [34195.564496956766, 34152.96743020938, 34135.751255257586, 34053.77648075063, 32815.37804177092, 33846.6327401742, 33294.86554827561, 32985.46798223975, 33262.202137426284, 33888.071738875304  …  34661.614733445305, 34079.44829399583, 33937.48866191337, 34750.512401621774, 35761.28543994881, 36026.6475737476, 35744.97128615692, 36215.72603264262, 36716.793627897336, 37709.49888979999], [34195.564496956766, 34118.55998375051, 34067.00569759841, 33950.95758610385, 32683.338182993648, 33676.481777199166, 33094.11405679792, 32753.55112442816, 32995.06515620766, 33582.041858357865  …  34279.42508000572, 33669.722978164, 33495.69077387455, 34263.57688742887, 35224.66372852936, 35450.29347230056, 35137.68823653588, 35564.57946653468, 36020.312733447805, 36956.91753149589], [33097.63671130043, 33257.40410172802, 33492.12876210006, 33014.64965094897, 31809.345855661217, 32975.97952670378, 32782.73066751457, 32107.8859912477, 32411.448598177678, 33157.523193574314  …  33601.964324844565, 33087.91941221676, 32782.532220071706, 33822.757529556504, 34516.41971921929, 34747.52221458437, 34689.82871224799, 34913.21287074801, 35641.63698611563, 36632.19645736154], [33097.63671130043, 33223.898891578116, 33424.67938763846, 32914.96820192729, 31681.35398766467, 32810.205438788274, 32585.067094833277, 31882.13930685709, 32151.144229282672, 32858.090610352345  …  33231.45870359863, 32690.1149021661, 32355.769543443643, 33348.82201923267, 33998.47804027988, 34191.63155330367, 34100.471826140674, 34285.485052946075, 34965.550739061815, 35901.11519191941], [29576.14777688488, 29605.974349916603, 29614.217443049056, 29582.630419695222, 29635.182055499936, 29656.937752639966, 29695.217441329067, 29675.910774589865, 29727.74450556735, 29786.09421819848  …  29990.776556106426, 30102.521191737887, 30161.769383523802, 30280.064440749495, 30324.344891904482, 30498.386977747825, 30622.72463939786, 30669.034761989584, 30765.513479739664, 30829.99628388419], [34346.71094688274, 34381.34847608993, 34419.45515479502, 34429.07416984341, 34470.95397043991, 34406.926797191096, 34539.118928934105, 34515.43354851272, 34607.31463506814, 34659.04444328411  …  34926.85106974452, 35093.06671699177, 35016.901480178494, 35205.01307450751, 35219.88361253535, 35477.18180987144, 35611.28828645602, 35631.03969896331, 35722.951829765596, 35849.712018831946], [28335.329312139656, 28363.90456052843, 28395.341785821136, 28403.277275174863, 28437.827248337177, 28385.006148764172, 28494.061935538186, 28474.521984386498, 28550.32198890895, 28592.99801553986  …  28813.932968073284, 28951.057454574046, 28888.222702490242, 29043.410894482986, 29055.67878216281, 29267.944497043427, 29378.579578894256, 29394.874087519525, 29470.699702906422, 29575.274248819092], [978.0617717704339, 979.048114923888, 981.5675855947828, 986.0625329389155, 986.0403651127124, 976.9493866526196, 983.3719349138455, 983.6590397660746, 984.6863522366477, 983.8765447648733  …  993.3984462291402, 1000.0498343240679, 994.9485892982402, 999.397010287232, 1002.5552452275931, 1012.4901195342021, 1017.211464486152, 1018.0139669477207, 1021.1036538931436, 1028.075700376977], [0.0019383188997990075, 0.00847608177264747, 0.0077643250704249045, 0.006529846626568414, 0.006939912406562998, 0.006452933948565054, 0.002880606283835574, 0.004203547082837433, 0.003289403000079405, 0.0033742248753081405  …  0.007309267290883881, 0.007029521856414478, 0.0035434249573584875, 0.0039316477743693845, 0.005849947621102025, 0.0036523582937388888, 0.004368378660495553, 0.0031609555547502666, 0.003635974820693999, 0.0011819741977778797], [2.3548476e6, 2.3625924349622033e6, 2.364771058029414e6, 2.3503376996849948e6, 2.326244438262859e6, 2.3397962900631027e6, 2.331007283565656e6, 2.3170893950703754e6, 2.3101868693135325e6, 2.3213129400393693e6  …  2.3420351003855336e6, 2.3258359649979486e6, 2.34523864091804e6, 2.351442502703695e6, 2.366246076207254e6, 2.3779867304830733e6, 2.3902523389719524e6, 2.3922574400180583e6, 2.4015478930209144e6, 2.4497038876438374e6], [0.0016459319014481277, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.002068522916658382, 0.001396372065683444, 0.002194850746544053, 0.0023827742321000157, 0.002959842489827623, 0.0033125650606751957, 0.0036749097127958693, 0.0035578930982402327, 0.00375991532307202, 0.005481000589196757], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.6059091613041 258.50369622511613 … 137.13538556670352 505.4138092341076; … ; 532.2792162247472 266.25453234334003 … 142.20135806616608 524.8543570685085; 529.6491411156939 266.04434521101103 … 142.62016769337987 526.2500481225468], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.2432663779743 … 136.99722836422478 504.904629508617; … ; 522.1824112485249 261.2039498573785 … 139.50394036511145 514.8983942529251; 519.0787521744271 260.7348072807106 … 139.7738482597034 515.7474960419414])])

Finally, we can plot baseline and shocked simulations

Te = T + 1
 StatsPlots.errorline(
     1:Te,
     data_vec_baseline.real_gdp,
@@ -50,4 +50,4 @@
     titlefont = 10,
     xlabel = "quarters",
     ylabel = "GDP",
-)
Example block output

Note that, importantly, once the function central_bank_rate has been changed, the model will use the new interest rate in all the simulations, unless the function is changed again. To restore the original interest rate, we can simply re-import the function central_bank_rate.

+)Example block output

Note that, importantly, once the function central_bank_rate has been changed, the model will use the new interest rate in all the simulations, unless the function is changed again. To restore the original interest rate, we can simply re-import the function central_bank_rate.

diff --git a/dev/index.html b/dev/index.html index 9926de1..dc132a0 100644 --- a/dev/index.html +++ b/dev/index.html @@ -38,6 +38,7 @@ [d1d4a3ce] BitFlags v0.1.9 [e1450e63] BufferedStreams v1.2.2 [d360d2e6] ChainRulesCore v1.25.0 + [ae650224] ChunkSplitters v3.1.0 [aaaa29a8] Clustering v0.15.7 [944b1d66] CodecZlib v0.7.6 [35d6a980] ColorSchemes v3.27.1 @@ -147,17 +148,17 @@ [6e34b625] Bzip2_jll v1.0.8+2 [83423d85] Cairo_jll v1.18.2+1 [ee1fde0b] Dbus_jll v1.14.10+0 - [2702e6a9] EpollShim_jll v0.0.20230411+0 - [2e619515] Expat_jll v2.6.4+0 + [2702e6a9] EpollShim_jll v0.0.20230411+1 + [2e619515] Expat_jll v2.6.4+1 ⌅ [b22a6f82] FFMPEG_jll v4.4.4+1 [f5851436] FFTW_jll v3.3.10+1 [a3f928ae] Fontconfig_jll v2.13.96+0 - [d7e528f0] FreeType2_jll v2.13.3+0 + [d7e528f0] FreeType2_jll v2.13.3+1 [559328eb] FriBidi_jll v1.0.14+0 [0656b61e] GLFW_jll v3.4.0+1 [d2c73de3] GR_jll v0.73.8+0 [78b55507] Gettext_jll v0.21.0+0 - [f8c6e375] Git_jll v2.46.2+0 + [f8c6e375] Git_jll v2.47.1+0 [7746bdde] Glib_jll v2.82.2+0 [3b182d85] Graphite2_jll v1.3.14+1 ⌃ [0234f1f7] HDF5_jll v1.14.2+1 @@ -171,7 +172,7 @@ [dd4b983a] LZO_jll v2.10.2+1 ⌅ [e9f186c6] Libffi_jll v3.2.2+1 [d4300ac3] Libgcrypt_jll v1.11.0+0 - [7e76a0d4] Libglvnd_jll v1.6.0+0 + [7e76a0d4] Libglvnd_jll v1.7.0+0 [7add5ba3] Libgpg_error_jll v1.50.0+0 [94ce4f54] Libiconv_jll v1.17.0+1 [4b2f31a3] Libmount_jll v2.40.2+0 @@ -201,18 +202,18 @@ [ffd25f8a] XZ_jll v5.6.3+0 [f67eecfb] Xorg_libICE_jll v1.1.1+0 [c834827a] Xorg_libSM_jll v1.2.4+0 - [4f6342f7] Xorg_libX11_jll v1.8.6+0 + [4f6342f7] Xorg_libX11_jll v1.8.6+1 [0c0b7dd1] Xorg_libXau_jll v1.0.11+1 [935fb764] Xorg_libXcursor_jll v1.2.0+4 [a3789734] Xorg_libXdmcp_jll v1.1.4+1 - [1082639a] Xorg_libXext_jll v1.3.6+0 + [1082639a] Xorg_libXext_jll v1.3.6+1 [d091e8ba] Xorg_libXfixes_jll v5.0.3+4 [a51aa0fd] Xorg_libXi_jll v1.7.10+4 [d1454406] Xorg_libXinerama_jll v1.1.4+4 [ec84b674] Xorg_libXrandr_jll v1.5.2+4 [ea2f1a96] Xorg_libXrender_jll v0.9.11+0 [14d82f49] Xorg_libpthread_stubs_jll v0.1.1+1 - [c7cfdc94] Xorg_libxcb_jll v1.17.0+0 + [c7cfdc94] Xorg_libxcb_jll v1.17.0+1 [cc61e674] Xorg_libxkbfile_jll v1.1.2+0 [e920d4aa] Xorg_xcb_util_cursor_jll v0.1.4+0 [12413925] Xorg_xcb_util_image_jll v0.4.0+1 @@ -226,7 +227,7 @@ [3161d3a3] Zstd_jll v1.5.6+1 [35ca27e7] eudev_jll v3.2.9+0 [214eeab7] fzf_jll v0.56.3+0 - [1a1c6b14] gperf_jll v3.1.1+0 + [1a1c6b14] gperf_jll v3.1.1+1 [477f73a3] libaec_jll v1.1.2+0 [a4ae2306] libaom_jll v3.9.0+0 [0ac62f75] libass_jll v0.15.2+0 @@ -288,4 +289,4 @@ [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 -Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

You can also download the manifest file and the project file.

+Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

You can also download the manifest file and the project file.