Moritz Marbach is a postdoctoral researcher at the Immigration Policy Lab at the University of Zurich which is the European branch of the Immigration Policy Lab at Stanford University. His research focuses on political methodology and the role of migration in politics. Marbach received his Ph.D. from the University of Mannheim in 2016. Over the last years, he has taught various courses in political methodology and international relations.

Course Content
Building on students’ knowledge in simple linear regression, this course introduces more advanced statistical techniques. We will focus on the following topics: 1) Subclassification, matching, and propensity score weighting as fundamental strategies to reduce confounding; 2) Common interpretations of the OLS estimator; 3) Robust inference with Heteroskedasticity- and autocorrelation-consistent covariance estimators as well as bootstrap variance estimators and Šidák/Bonferroni correction; 4) Specification and interpretation of interactions and the mechanics of smoothing, splines, and local linear regression for flexible functional forms; 5) Classical exposition of various biases and introduction to causal graphs to construct appropriate conditioning sets; 6) Instrumental variable estimation with two-stage least-square, inference with weak instruments, and the Heckman estimator for selected samples; 7) Fixed and random effect as well as difference-in-difference estimator; 8) Maximum likelihood estimator with examples for limited dependent variables and a discussion on structural parameters vs. causal estimands. The course combines a theoretical introduction to each topic with lab sessions in which students perform replications of published work in Economics and Political Science using STATA. Students are encouraged to bring their own data sets and present their research projects and empirical analyses throughout the course.

Course Objectives
Students will develop a deeper understanding of the statistical problems that arise in applied research. It will give participants the skills to i) Make sensible choices about which data to collect; ii) Select an appropriate estimator for their estimands and the data at hand; iii) Precisely state the assumptions on which estimates are based; and iv) Conduct sensible, robust inferences and interpret their estimates accurately. More generally, students will be able to decide which variables they have to control for (and which they do not) and develop an understanding of what it means when regression results are distorted by selection bias, measurement error or omitted variable bias.

Course Prerequisites
This course is targeted at social and political scientists with a strong interest in applied empirical research and data analysis. The course is designed for students who already have training in statistics, including a good understanding of simple linear regression and statistical tests, as well as basic knowledge of matrix algebra, calculus, and probability theory. Participants must be familiar with STATA and its command structure and be able to write their own do-files. R users are also very welcome.

Core Reading
– Angrist, J. D. and J.-S. Pischke (2009). Mostly Harmless Econometrics. An Empiricist’s Companion. Princeton: Princeton University Press.

Course Structure:

Day 1 Cause and Effect
Day 2 Subclassification, Weighting and Matching
Day 3 Perspectives on OLS
Day 4 Robust Inference with OLS
Day 5 Functional Form Specification
Day 6 Control Variables
Day 7 Instrumental Variables
Day 8 DID and Panel Data
Day 9 Limited Dependent Variables
Day 10 Review and Student Presentations

Essential Course Readings:

Day 1
– Angrist and Pischke (2009), Chapter 1-2.
– Holland (1986).
– Agresti and Finlay (2009), Chapter 5-6.

Day 2

– Cochran (1968).
– Rosenbaum (2009), Chapter 7, 8.1-8-3, 9.

Day 3
– Angrist and Pischke (2009), Chapter 3.
– Wooldridge (2010), Chapter 2.

Day 4
– Wooldridge (2009), Chapter 5, 8.
– Angrist and Pischke (2009), Chapter 8.

Day 5
– Keele (2008), Chapter 2.1-2.2, 3.1-3.3.
– Brambor, Clark, and Golder (2006).

Day 6

– Morgan and Winship (2007), Chapter 3.
– Wooldridge (2010), 4.3-4.4

Day 7
– Angrist and Pischke (2009), Chapter 4.
– Wooldridge (2010), Chapter 5.

Day 8
– Angrist and Pischke (2009), Chapter 5.
– Wooldridge (2010), Chapter 10.

Day 9
– King (1998), Chapter 4, 5.1-5.3
– Wooldridge (2010), Chapter 13.1-13.6, 15.


References:

Agresti, Alan, and Barbara Finlay. 2009. Statistical Methods for the Social Sciences. 4th ed. Upper Saddle River: Pearson.

Angrist, Joshua D., and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics. an Empiricist’s Companion. Princeton: Princeton University Press.

Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14: 63–82.

Cochran, W. G. 1968. “The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies.” Biometrics 24 (2): 295–313.

Freedman, David A. 1991. “Statistical Models and Shoe Leather.” Sociological Methodology 21: 291–313.
Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60.

Keele, Luke. 2008. Semiparametric Regression for the Social Sciences. Chichester: Wiley.

King, Gary. 1998. Unifying Political Methodology. the Likelihood Theory of Statistical Inference. Ann Arbor: The University of Michigan Press.

Morgan, Stephen L., and Christopher Winship. 2007. Counterfactual and Causal Inference. Methods and Principles for Social Research. Cambridge: Cambridge University Press.

Rosenbaum, Paul R. 2009. Design of Observational Studies. 2nd ed. New York: Springer.

Wooldridge, Jeffrey M. 2009. Introduction to Econometrics. a Modern Approach. 4th ed. South Western, Cengage Learning.

———. 2010. Econometric Analysis of Cross Section and Panel Data. 2nd ed. Cambridge: MIT Press.