Panel / Longitudinal Data Analysis

The course covers most of the traditional panel data estimation techniques for micro panels in which the number of individuals (or firms etc.) is large, but the number of time periods is quite small. It focuses on the treatment of unobserved individual specific heterogeneity and discusses the difference between random and fixed effects model specifications.

Attention is given to the estimation of models with explanatory variables that are not strictly exogenous. This means that there can be feedback from the process to be explained to the explanatory variables (for example outputs and inputs in a production function, the effect of previous cigarette consumption on current consumption), or simultaneous determination. In these cases models in first differences can be estimated with instrumental variables estimation techniques, better know as the Arellano-Bond GMM DIF estimation method. Moment conditions for the model in levels will also be considered, resulting in the Blundell-Bond SYS GMM estimator.