Introduction

Longitudinal data or panel data are observations on a cross-section of individual units such as persons, households, firms, and regions that are observed over several time periods. The data structure is similar to that of multivariate data considered in Chapter 8. Analysis is simpler than for multivariate data because for each individual unit the same outcome variable is observed, rather than several different outcome variables. Analysis is more complex because this same outcome variable is observed at different points in time, introducing time series data considerations presented in Chapter 7.

In this chapter we consider longitudinal data analysis if the dependent variable is a count variable. Remarkably, many count regression applications are to longitudinal data rather than simpler cross-section data. Econometrics examples include the number of patents awarded to each of many individual firms over several years, the number of accidents in each of several regions, and the number of days of absence for each of many persons over several years. A political science example is the number of protests in each of several different countries over many years. A biological and health science example is the number of occurrences of a specific health event, such as seizure, for each of many patients in each of several time periods.

A key advantage of longitudinal data over cross-section data is that they permit more general types of individual heterogeneity. Excellent motivation was provided by Neyman (1965), who pointed out that panel data enable one to control for heterogeneity and thereby distinguish between true and apparent contagion.