Moritz Marbach is an Assistant Professor at The Bush School of Government & Public Service, Texas A&M University. His research focuses on the role of immigration in politics and political methodology.
Building on students’ knowledge in simple linear regression, this course introduces more advanced statistical methods. 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 omitted variable bias and introduction to causal graphs to select control variables; 5) Instrumental variable estimation with two-stage least-squares; 6) Two-way fixed effects and the difference-in-difference estimator with panel data; 7) Mediation and moderation analysis. 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.
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 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 = S
Maximum Likelihood Estimation: M
Stata = M
e = elementary, m = moderate, s = strong
POTENTIAL ESS APPLICANTS ARE TO BE ADVISED THAT RECORDINGS WILL NOT BE MADE AVAILABLE FOR THIS COURSE.