draft lecture mixtape book

environment.yml

@@ -37,3 +37,4 @@ dependencies:
   - grmpy
   - sphinx_rtd_theme
   - sphinxcontrib-bibtex
+  - plotnine

lectures/difference-in-difference/Untitled.ipynb

@@ -0,0 +1,50 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "12c58218",
+   "metadata": {},
+   "source": [
+    "# Difference in difference\n",
+    "----------------\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1ea6045",
+   "metadata": {},
+   "source": [
+    "# References\n",
+    "\n",
+    "* **Athey, S., & Imbens, G. (2021)**. [Design-based analysis in difference-in-differences settings with staggered adoption](https://www.sciencedirect.com/science/article/abs/pii/S0304407621000488), *Journal of Econometrics*.\n",
+    "\n",
+    "\n",
+    "* **Bertrand, M., Dufflo, E., & Mullainathan, S. (2004)**. [How much should we trust differences-in-differences estimates?](https://ideas.repec.org/a/oup/qjecon/v119y2004i1p249-275..html), *The Quarterly Journal of Economics*, 119(1), 249-275.\n",
+    "\n",
+    "\n",
+    "* **Goodman-Bacon, A. (2021)**. [Difference-in-differences with variation in treatment timing](https://www.sciencedirect.com/science/article/abs/pii/S0304407621001445), *Journal of Econometrics*, 255(2), 254-277."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

lectures/experiments/Untitled.ipynb → lectures/experiments/notebook.ipynb

@@ -5,7 +5,7 @@
    "id": "3b9d7376",
    "metadata": {},
    "source": [
-    "# Experiments\n",
+    "# Randomized Experiments\n",
     "-------------------"
    ]
   },
@@ -14,7 +14,12 @@
    "id": "db4554c6",
    "metadata": {},
    "source": [
-    "* **Athey, S., & Imbens, G. (2017)**. [Chapter 3 - The econometrics of randomized experiments](https://www.sciencedirect.com/science/article/abs/pii/S2214658X16300174), in *Handbook of Economic Field Experiments*, 73-140."
+    "# References \n",
+    "\n",
+    "* **Athey, S., & Imbens, G. (2017)**. [Chapter 3 - The econometrics of randomized experiments](https://www.sciencedirect.com/science/article/abs/pii/S2214658X16300174), in *Handbook of Economic Field Experiments*, 73-140.\n",
+    "\n",
+    "\n",
+    "* **Freedman, D.A. (2008)**. [On regression adjustments to experimental data](https://www.sciencedirect.com/science/article/pii/S019688580700005X), *Advances in Applied Mathematics*, 40(2), 180-193."
    ]
   }
  ],

lectures/index.rst

@@ -37,6 +37,17 @@ We explore the usefulness of causal graphs for the visualization of complex caus
 
    causal-graphs/notebook.ipynb
 
+=========================
+Randomized Experiments
+=========================
+
+A lecture on randomized experiments will be part of the next iteration of the OSE data science course, summer semester 2022. Details on this lecture will be realized soon.
+
+.. toctree::
+   :maxdepth: 1
+
+   experiments/notebook.ipynb
+
 =======================
 Conditioning estimators
 =======================
@@ -137,15 +148,15 @@ We study regression discontinuity design in more detail. We discuss identificati
    regression-discontinuity/notebook.ipynb
 
 =============================
-Generalized method of moments
+Difference-in-Difference
 =============================
 
-We review the basic ideas behind the generalized method of moments (GMM) and implement some numerical examples. After introducing its basic setup, we discuss the GMM criterion function and how alternative estimation strategies are cast as GMM estimation problems. We then turn to the issues of identification and the role of the weighing matrix. Throughout, we practice the basic derivations involved in the GMM approach using an instrumental variables setup.
+A lecture on difference-in-difference method will be part of the next iteration of the OSE data science course, summer semester 2022. Details on this lecture will be realized soon.
 
 .. toctree::
    :maxdepth: 1
 
-   generalized-moments/notebook.ipynb
+   difference-in-difference/notebook.ipynb
 
 
 =============================

lectures/instrumental-variable/notebook.ipynb

@@ -1227,10 +1227,25 @@
     "* **Angrist, J. D., & Krueger, A. B. (1991)**. [Does compulsory school attendance affect schooling and earnings?](https://academic.oup.com/qje/article-abstract/106/4/979/1873496?redirectedFrom=fulltext). *The Quarterly Journal of Economics*, 106(4), 979-1014. \n",
     "\n",
     "\n",
+    "* **Angrist, J. D., Imbens, G. W., & Rubin, D.B. (1996)**. [Identification of causal effects using instrumental variables ](https://www.jstor.org/stable/2291629?refreqid=excelsior%3A77b1779497b7c2f47f2a1d9cc96265be). *Journal of the American Statistical Association*, 91(434), 444-455. \n",
+    "\n",
+    "\n",
+    "* **Angrist, J. D., & Imbens, G. W. (1999)**. [Comment on James Heckman, \"Instrumental Variables: A Study of Implicit Behavioral Assumption Used in Making Program Evaluations](https://scholar.harvard.edu/imbens/publications/comment-james-heckman-instrumental-variables-study-implicit-behavioral-assumptio). *Journal of Human Resources*, 34, 823– 827.\n",
+    "\n",
+    "\n",
+    "* **Deaton, A. S. (2009)**. [Instruments of development: randomization in the tropics, and the search for the elusive keys to economic development](https://www.nber.org/papers/w14690). *National Bureau of Economic Research*, Working Paper 14690. \n",
+    "\n",
+    "\n",
     "* **Heckman, J. J. (1997)**. [Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations](https://www.jstor.org/stable/146178?seq=1). *The Journal of Human Resources*, 32(3), 441–462.\n",
     "\n",
     "\n",
-    "* **Angrist, J. D., & Imbens, G. W. (1999)**. [Comment on James Heckman, \"Instrumental Variables: A Study of Implicit Behavioral Assumption Used in Making Program Evaluations](https://scholar.harvard.edu/imbens/publications/comment-james-heckman-instrumental-variables-study-implicit-behavioral-assumptio). *Journal of Human Resources*, 34, 823– 827. \n",
+    "* **Heckman, J. J., & Urzúa, S. (2010)**. [Comparing IV with structural models: What simple IV can and cannot identify](https://www.sciencedirect.com/science/article/abs/pii/S0304407609001973), *Journal of Econometrics*, 156(1), 27-37.\n",
+    "\n",
+    "\n",
+    "* **Imbens, G. W. (2010)**. [Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)](https://www.aeaweb.org/articles?id=10.1257/jel.48.2.399). *Journal of Economic Literature*, 48(2), 399-423. \n",
+    "\n",
+    "\n",
+    "* **Imbens, G. W. (2014)**. [Instrumental variables: An econometrician’s perspective](https://projecteuclid.org/journals/statistical-science/volume-29/issue-3/Instrumental-Variables-An-Econometricians-Perspective/10.1214/14-STS480.full). *Statistical Science*, 29(3), 323-358. \n",
     "\n",
     "\n",
     "* **Heckman, J. J. (1999)**. [Instrumental Variables: Response to Angrist and Imbens](https://www.jstor.org/stable/146419#metadata_info_tab_contents). *The Journal of Human Resources*, 34(4), 828-837.\n",
@@ -1256,7 +1271,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.8"
   },
   "pycharm": {
    "stem_cell": {

lectures/matching-estimators/notebook.ipynb

@@ -2494,6 +2494,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "* **Buso, M., DiNardo, J., & McCrary, J. (2014)**. [New Evidence on the finite sample properties of propensity score reweighting and matching estimators](https://www.jstor.org/stable/43554965), *The Review of Economics and Statistics*, 96(5), 885-897. \n",
+    "\n",
+    "\n",
     "* **Heckman, J. J., Ichimura, H., Smith, J. A. and Todd, P. (1998)**. [Characterizing selection bias using experimental data](https://www.jstor.org/stable/2999630?seq=1). *Econometrica*, 66(5), 1017–98.\n",
     "\n",
     "\n",
@@ -2515,6 +2518,9 @@
     "* **Rosenbaum, P. (2020)**. [Modern algorithms for matching in observational studies](https://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-031219-041058), 7, 143-176.\n",
     "\n",
     "\n",
+    "* **Rubin, D. (2006)**. [The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials](The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials), *Statistics in Medicine*, 26(1), 20-36.\n",
+    "\n",
+    "\n",
     "* **Smith, J. A. and Todd, P. (2005)**. [Does matching evercome Lalonde’s eritique of nonexperimental estimators?](https://www.sciencedirect.com/science/article/abs/pii/S030440760400082X). *Journal of Econometrics*, 125(1–2), 305–53.\n",
     "\n",
     "* **Stuart, E. (2010)**. [Matching methods for causal inference: a review and a look forward](https://projecteuclid.org/journals/statistical-science/volume-25/issue-1/Matching-Methods-for-Causal-Inference--A-Review-and-a/10.1214/09-STS313.full), *Statistical Science*, 25(1), 1-21."