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Modify the callings of LinearStateSpace.stationary_distributions #380

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Modify the callings of LinearStateSpace.stationary_distributions #380

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4 changes: 2 additions & 2 deletions source/rst/linear_models.rst
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Expand Up @@ -799,7 +799,7 @@ Let's now try with 500,000 observations, showing only the histogram (without rot
ar = LinearStateSpace(A_2, C_2, G_2, mu_0=np.ones(4))
fig, ax = plt.subplots()
x, y = ar.simulate(sample_size)
mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions()
mu_x, mu_y, Sigma_x, Sigma_y, Sigma_yx = ar.stationary_distributions()
f_y = norm(loc=float(mu_y), scale=float(np.sqrt(Sigma_y)))
y = y.flatten()
ygrid = np.linspace(ymin, ymax, 150)
Expand Down Expand Up @@ -1006,7 +1006,7 @@ This picture shows cross-sectional distributions for :math:`y` at times
ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))

if steady_state == 'True':
μ_x, μ_y, Σ_x, Σ_y = ar.stationary_distributions()
μ_x, μ_y, Σ_x, Σ_y, Σ_yx = ar.stationary_distributions()
ar_state = LinearStateSpace(A, C, G, mu_0=μ_x, Sigma_0=Σ_x)

ymin, ymax = -0.6, 0.6
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2 changes: 1 addition & 1 deletion source/rst/perm_income_cons.rst
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Expand Up @@ -382,7 +382,7 @@ First, we create the objects for the optimal linear regulator
μ_z0 = np.array([[1.0], [0.0], [0.0]])
Σ_z0 = np.zeros((3, 3))
Lz = qe.LinearStateSpace(A, C, G, mu_0=μ_z0, Sigma_0=Σ_z0)
μ_z, μ_y, Σ_z, Σ_y = Lz.stationary_distributions()
μ_z, μ_y, Σ_z, Σ_y, Σ_yx = Lz.stationary_distributions()

# Mean vector of state for the savings problem
mxo = np.vstack([μ_z, 0.0])
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2 changes: 1 addition & 1 deletion source/rst/samuelson.rst
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Expand Up @@ -1348,7 +1348,7 @@ methods and attributes) to add more functions to use
# values for simulation
if stationary == True:
try:
self.μ_x, self.μ_y, self.σ_x, self.σ_y = \
self.μ_x, self.μ_y, self.σ_x, self.σ_y, self.σ_yx = \
self.stationary_distributions()
self.μ_0 = self.μ_y
self.Σ_0 = self.σ_y
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