This one-week summer course builds on ordinary least square (OLS) regression and extends it to the econometric models and techniques commonly used to analyze panel data.
The course begins with a quick review of the standard OLS framework. It then moves on to simple panel data methods, such as 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.
Overall, this course aims to strike a balance between statistical theory and practical application. You learn to use panel data in your research and develop an understanding and appreciation for the science behind these methods.
