Moritz Marbach is a postdoctoral researcher at the Immigration Policy Lab at the ETH 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.
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 cluster-robust standard errors as well as bootstrap estimators; 4) Classical exposition of various biases and introduction to causal graphs to select control variables; 5) Specification and interpretation of interactions and non-linear effects; 6) Instrumental variable estimation with two-stage least-squares and the Heckman estimator for selected samples; 7) Two-way fixed effects and the difference-in-difference estimator with panel data; 8) Generalized linear models 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.
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.
This course is targeted at social and political scientists with a strong interest in applied empirical research and data analysis. Participants must be familiar with STATA and its command structure and be able to write their own do-files. R users are also welcome. The course is designed for students with basic training in statistics including linear regression and hypothesis testing. It is also essential that students have basic knowledge of matrix algebra, calculus, and probability theory to follow the lectures.
– Angrist, J. D. and J.-S. Pischke (2009). Mostly Harmless Econometrics. An Empiricist’s Companion. Princeton: Princeton University Press.
Representative Background Reading
– Alan Agresti and Barbara Finlay. Statistical Methods for the Social Sciences. Pearson, Upper Saddle River, 4th edition, 2009, Chapter 1-9.
Background knowledge required
OLS = e
Stata = m
e = elementary, m = moderate, s = strong