Panel Regression Models

Instructor: Dr. David Pupovac (Lazarsky University, Poland)

 

Description:

The methods of analyzing panel (pooled cross-sectional time series) data are applicable to a large selection of research questions and travel across disciplines. The goal of the course is to provide a comprehensive introduction to these methods and to train students in extracting inferences from data collected over time and across space. We will work with data which collect time series aggregated across units such as individuals, countries or firms. The first session approaches the problem of panel data from the linear regression perspective. We will particularly discuss feasible generalized least squares, clustered Huber/White/sandwich estimator and panel-corrected standard errors. The focus of the second session is on addressing the theory and a variety of strategies in estimation of fixed effects models. The third session addresses the distinction between random effects and fixed effects models, discusses the relevant diagnostics tests and random coefficients model. The focus of the fourth session is modeling of dynamics. The fifth and sixed session are dedicated to a variety of advanced topics in panel data analysis. In these sessions we will discuss topics such as: spatial modeling, the extensions of panel data analysis to binary and censored dependent variables, instrumental variables estimation and others.

 

Required literature for preparation:

Session 1

Beck, N., & J. N. Katz, 1995: What to do (and not to do) with time-series cross-section data. American Political Science Review 89 (3): 634-647.

Session 2

Stimson, J. A., 1985: Regression in Space and Time: A Statistical Essay. American Journal of Political Science 29 (4): 914-947.

Session 3

Clark, T. S. & D. A. Linzer, 2015: Should I Use Fixed or Random Effects? Political Science Research and Methods 3 (2): 399-408.

Session 4

Beck, N. & J. N. Katz, 2011: Modeling Dynamics in Time-Series-Cross-Section Political Economy Data. Annual Review of Political Science 14: 331-352.

Session 5

Beck, N., K. S. Gleditsch & K. Beardsley, 2006: Space is more than geography: Using spatial econometrics in the study of political economy. International Studies Quarterly 50 (1): 27-44.

Session 6

Carter, D. B. & C. S. Signorino, 2010: Back to the Future: Modeling Time Dependence in Binary Data. Political Analysis 18 (3): 271-292.