Microeconometrics

... introductory course to microeconmetrics. The course is designed as to supplement and emphasize selected topics from the two textbooks below. As such, it is important that students read the relevant chapters in advance to get most out of the class.

Throughout the course we will make heavy use of Python and its SciPy ecosystem and Jupyter Notebooks throughout the course and so we provide some useful resources below. For further information, please do not hesitate to contact us.

Counterfactual approach to causal analysis

• Winship, C., and Morgan, S. L. (2014). Counterfactuals and causal inference: Methods and principles for social research. Cambridge, England: Cambridge University Press.

• Frölich, M., and Sperlich, S. (2019). Impact evaluation: Treatment effects and causal analysis. Cambridge, England: Cambridge University Press.

• Angrist, J. D., and Pischke, J. (2009). Mostly harmless econometrics: An empiricists companion. Princeton, NJ: Princeton University Press.

Potential outcome model

• Heckman, J. J., and Vytlacil, E. J. (2007a). Econometric evaluation of social programs, part I: Causal effects, structural models and econometric policy evaluation. In J. J. Heckman, and E. E. Leamer (Eds.), Handbook of Econometrics (Vol. 6B, pp. 4779–4874). Amsterdam, Netherlands: Elsevier Science.

• Imbens G. W., and Rubin D. B. (2015). Causal inference for statistics, social, and biomedical sciences: An introduction.. Cambridge, England: Cambridge University Press.

Directed graphs

• Pearl, J. (2014). Causality. Cambridge, England: Cambridge University Press.

• Peters, J., Janzig, D., and Schölkopf, B. (2018) Elements of causal inference: Foundations and learning algorithms. Cambridge, MA: The MIT Press.

Please use the table of content to navigate the rest of the material.

1. Lectures
2. Problem sets
3. Special focus
4. Resources
5. Iterations

We collect a list of additional, more general, reading recommendations here.

Lectures

We provide the lectures in the form of a Jupyter notebook.

Introduction

We briefly introduce the course and discuss some basic ideas about counterfactuals and causal inference.

Tools for data science

This guest lecture by the team of OpenSourceEconomics presents a basic overview on the scientific Python ecosystem as we will heavily rely on it throughout the course.

Potential outcome model

We discuss the core model of the course.

Causal graphs

We explore the usefulness of causal graphs for the visualization of complex causal systems and the clarification of alternative identification strategies for causal effects. After establishing their basic notation and some key concepts, we link them to structural equations and the potential outcome model.

Models of causal exposure and identification criteria for conditioning estimators

We study the basic conditioning strategy for the estimation of causal effects.

Matching estimators of causal effects

We review the fundamental concepts of matching such as stratification of data, weighting to achieve balance, and propensity scores.

Regression estimators of causal effects

We study the most common form of data analysis.

Self-selection, heterogeneity, and causal graphs

We lay the groundwork to estimate causal effects if simple conditioning on observed variables that lie along all back-door paths will not suffice.

Instrumental variable estimators of causal effects

We study the use of instrumental variable estimators.

Mechanisms and causal explanation

We study front-door identification that allow (under certain conditions) to provide a causal account of the effect of D on Y.

Repeated observations and the estimation of causal effects

We now explore models in which we have multiple observations at different points in time. Due to its similar structure, we also look at the sharp and fuzzy regression discontinuity design.

Regression discontinuity design

We study regression discontinuity design in more detail. We discuss identification, issues in interpretation, and challenges to application based on the seminal review by Lee & Lemieux (2010). We reproduce and check the robustness of some of the results in Lee (2008).

Generalized method of moments

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.

Problem sets

We will work on several problem sets throughout the course.

Potential outcome model

We explore the potential outcome model using observed and simulated data inspired by the National Health Interview Survey. The accompanying data sets are available here here.

Special focus

We discuss selected topics in more details based on student demands.

Nonstandard standard errors

We review issues in the construction of standard errors such as the potential bias of robust standard error estimates, clustering, and serial correlation based on the material presented in Angrist & Pischke (2009). We use this opportunity to discuss the research reported in Krueger (1999).

Resources

We provide some additional resources that are useful for our course work.

Textbooks

• Wooldridge, J. M. (2009). Econometric analysis of cross section and panel data. Cambridge, MA: The MIT Press.

• Angrist, J. D., & Pischke, J. (2014). Mastering 'metrics. Princeton, NJ: Princeton University Press.

• Stock, J.H., & Watson, M.W. (2003). Introduction to econometrics. Pearson.

Datasets

The textbooks above provide an impressive amount of data from research articles. We provide them in a central place here.

Articles

We repeatedly touch on some selected articles throughout the class.

• Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery: Evidence from social security administrative records. American Economic Review, 80(3), 313-336.

• Angrist, J. D., & Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? The Quarterly Journal of Economics, 106(4), 979-1014.

• Bertrand, M; Duflo E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119, 249-275.

• Krueger, A. B. (1999). Experimental estimates of education prodcution functions. The Quarterly Journal of Economics, 114, 497-532.

• Lee, D. S. (2008). Randomized experiments from non-random selection in us house elections. Journal of Econometrics, 142(2), 675–697.

• Lee, D. S, & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48, 281-355.

• Rosenzweig, M. R., & Wolpin, K. I. (2000). Natural "natural experiments" in economics. Journal of Economic Literature, 38(4), 827-874.

Tools

We maintain a list of useful resources around the tooling used in the course here.

Software packages

• grmpy (2018). grmpy: A Python package for the simulation and estimation of the generalized Roy model. Retrieved from http://doi.org/10.5281/zenodo.1162640

• respy (2018). respy: A Python package for the simulation and estimation of a prototypical finite-horizon dynamic discrete choice model. Retrieved from http://doi.org/10.5281/zenodo.1189209

Iterations

• Summer Quarter 2019, Graduate Program at the University of Bonn, please see here for details.