This book is a companion to Baltagi's (2008) leading graduate ecometrics textbook on panel data entitled Ecometric Analysis of Panel Data, 4 th Edition. The book guides the student of panel data ecometrics by solving exercises in a logical and pedagogical manner, helping the reader understand, learn and apply panel data methods. It is also a helpful tool for those who like to learn by solving exercises and running software to replicate empirical studies. It works as a complementary study guide to Baltagi (2008) and also as a stand alone book that builds up the reader's confidence in working out difficult exercises in panel data ecometrics and applying these methods to empirical work. The exercises start by providing some background information on partitioned regressions and the Frisch-Waugh-Lovell theorem. Then it goes through the basic material on fixed and random effects models in a one-way and two-way error components models: basic estimation, test of hypotheses and prediction. This include maximum likelihood estimation, testing for poolability of the data, testing for the significance of individual and time effects, as well as Hausman's test for correlated effects. It also provides extensions of panel data techniques to serial correlation, spatial correlation, heteroskedasticity, seemingly unrelated regressions, simultaneous equations, dynamic panel models, incomplete panels, measurement error, count panels, rotating panels, limited dependent variables, and n-stationary panels. The book provides several empirical examples that are useful to applied researchers, illustrating them using Stata and EViews showing the reader how to replicate these studies. The data sets are provided on the Wiley web site: www.wileyeurope.com/college/baltagi .
Badi H. Baltagi is Distinguished Professor of Economics, and Senior Research Associate at the Center for Policy Research, Syracuse University. He is a fellow of the Journal of Econometrics, a recipient of the Multa and Plura Scripsit Awards from Econometric Theory, and the Journal of Applied Econometrics Distinguished Authors Award.