This book describes regression methods for count data, where the response variable is a non-negative integer. The methods are relevant for analysis of counts that arise in both social and natural sciences.

Despite their relatively recent origin, count data regression methods build on an impressive body of statistical research on univariate discrete distributions. Many of these methods have now found their way into major statistical packages, which has encouraged their application in a variety of contexts. Such widespread use has itself thrown up numerous interesting research issues and themes, which we explore in this book.

The objective of the book is threefold. First, we wish to provide a synthesis and integrative survey of the literature on count data regressions, covering both the statistical and econometric strands. The former has emphasized the framework of generalized linear models, exponential families of distributions, and generalized estimating equations, while the latter has emphasized nonlinear regression and generalized method of moment frameworks. Yet between them there are numerous points of contact which can be fruitfully exploited. Our second objective is to make sophisticated methods of data analysis more accessible to practitioners with different interests and backgrounds. To this end we consider models and methods suitable for cross-section, time series, and longitudinal data. Detailed analyses of several data sets as well as shorter illustrations, implemented from a variety of viewpoints, are scattered throughout the book to put empirical flesh on theoretical or methodological discussion. We draw on examples from, and give references to, works in many applied areas.