RSS01.D8 Panel Data Analysis - Confirmed

This course builds on OLS regression and extends it to the various econometric models and techniques that are commonly used to analyze panel data. In addition, the course places a special emphasis on the connections between these methods and causal inference, which is the primary goal of all social science research. Panel data are particularly well suited for causal inference as they allow you to control for unit-specific factors/individual heterogeneity, such as geography and culture, which may be unobserved and can be difficult to measure. Furthermore, panel data usually provide you with more degrees of freedom and more sample variability than purely cross-sectional or time-series data, and hence improve the efficiency of parameter estimates.

The course begins with a quick review of the standard ordinary least square (OLS) framework. It then moves on to simple panel data methods, fixed and random effect estimators. The remainder of the course focuses on more advanced methods, such as methods for the study of clustered samples, panel instrumental variable methods, and dynamic panel regression, that deal with violations of the OLS assumptions.
Theoretical lectures are complemented by applied lab sessions that put these methods into practice. Specifically, the course is divided into three parts. The first part involves a thorough discussion of the logics and assumptions underlying panel data methods. You learn how the development of more advanced methods is driven by the need to address potential violations of these assumptions.

The second part focuses on the various statistical approaches and 'tricks' available to social scientists to deal with such violations and problems hidden in their data, allowing you to estimate effects that are as close as possible to the true causal effects.
The final part of the course focuses on applying the wide range of panel data methods discussed in the previous parts to substantive research questions of interest. You learn how these methods can be used to provide answers to your own research questions. In this context, you are encouraged to bring and work with your own panel data during the course’s applied lab sessions.

This course aims to strike a balance between statistical theory and practical application, and you have the opportunity to learn and practice how to use panel data methods with the help of the popular statistical software R and Stata as well as to develop an understanding and appreciation for the science behind these methods.


19 June 2023 - 23 June 2023
Course Fee

Regular: €995
Students & PhD's: €645

Early Bird Regular: €895 (application deadline* April 1st) 
Early Bird Students & PhD's: €580,50 (application deadline* April 1st)

Scholarships and discounts Find more information here
Application deadline

May 15th

*Your application is only completed when the course fee has been paid

Course leader Andrew X. Li
Level of participant
  • Master
  • PhD
  • Post-Doc
  • Professional
Admission requirements ​​This course presumes a working knowledge of OLS regression. Participants should be familiar with the OLS assumptions and related statistical concepts such as heteroskedasticity. A background in linear algebra would be helpful, but is not required. Participants should also have some familiarity with Stata and/or R.
Admission documents
  • ​To get the student/PhD discount you need to upload a copy of your Student card or other proof of registration
  • If you are not a student/PhD, you can upload an empty document under 'Student Card'.
Mode of Study On Campus
ECTS 2 or 4 Find more information here
Location Radboud University