This panel/longitudinal data analysis course covered 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 focused on the treatment of unobserved individual specific heterogeneity and the difference between random and fixed effects model specifications.
Attention was 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 was also be considered, resulting in the Blundell-Bond SYS GMM estimator.
The course was a mixture of lectures and applied sessions. We applied the various techniques using real data on their computers. The software application used was Stata. A basic knowledge of econometrics was assumed.