The aim of this volume is to provide a general overview of the
econometrics of panel data, both from a theoretical and from an applied
viewpoint. Since the pioneering papers by Edwin Kuh (1959), Yair Mundlak
(1961), Irving Hoch (1962), and Pietro Balestra and Marc Nerlove (1966),
the pooling of cross sections and time series data has become an
increasingly popular way of quantifying economic relationships. Each
series provides information lacking in the other, so a combination of
both leads to more accurate and reliable results than would be
achievable by one type of series alone. Over the last 30 years much work
has been done: investigation of the properties of the applied estimators
and test statistics, analysis of dynamic models and the effects of
eventual measurement errors, etc. These are just some of the problems
addressed by this work. In addition, some specific diffi- culties
associated with the use of panel data, such as attrition, heterogeneity,
selectivity bias, pseudo panels etc., have also been explored. The first
objective of this book, which takes up Parts I and II, is to give as
complete and up-to-date a presentation of these theoretical developments
as possible. Part I is concerned with classical linear models and their
extensions; Part II deals with nonlinear models and related issues:
logit and pro bit models, latent variable models, duration and count
data models, incomplete panels and selectivity bias, point processes,
and simulation techniques.