Panel Data Econometrics Summer School

Summer School


The course gives the participants the opportunity to have theory sessions in the morning combined with practical cases in the afternoon. Participants will work with Stata on their own laptop, and Professor Verbeek will virtually walk around to assist and give feedback. In this online edition, all sessions take place via Zoom.

It’s been assumed that participants are familiar with basic econometric methods and models, including linear regression, serial correlation, simply hypothesis testing, heteroskedasticity, endogeneity and instrumental variables, and simple dynamic modeling. The course will avoid matrix notations as much as possible. The course will be supported by class slides, empirical exercises and reading material (book chapters and journal articles).


Panel data analysis is concerned with data that run over time for different units of observations. That can be individuals, households, firms, regions, countries, etc. The repeated nature of the data enables the use of more realistic econometric models (for example, incorporating dynamics at the individual level), more robust estimation methods (e.g. controlling for unobserved heterogeneity) and more powerful testing procedures (e.g. testing for long-run purchasing power parity by pooling a number of countries). On the other hand, the panel nature of the data introduces some complications, including possible parameter heterogeneity, serial correlation or error component structures of the error terms, and cross-sectional dependence. These issues are particularly disturbing in nonlinear or dynamic models. The course will provide an insightful and useful elaboration of the state of the art in empirical panel data analysis at an introductory level. Starting from a general introduction, discussing advantages and disadvantages of panel data, static models with fixed effects or random effects, instrumental variables estimators, clustered standard errors and robust inference, the course focuses on (micro-economic) dynamic models, where the time dimension is typically limited and the number of cross-sectional units is large. Topics covered include fixed effects and random effects models, instrumental variables, estimation by the Generalized Method of Moments (GMM), and models with limited dependent variables. 


PhD candidates and Research Master students receive 3 ECTS for active participation and assignments that will be given during the course. Participants will work with Stata on three different assignments (with data sets) during the afternoon sessions.


Notation and structure of the course will follow Chapter 10 of Verbeek (2017), A Guide to Modern Econometrics, 5th edition, John Wiley and Sons.

Additional info

The timetable for this course can be found here.

This course is fully booked.