Note publique d'information : The book provides graduate students and researchers with an up-to-date survey of statistical
and econometric techniques for the analysis of count data, with a focus on conditional
distribution models. Proper count data probability models allow for rich inferences,
both with respect to the stochastic count process that generated the data, and with
respect to predicting the distribution of outcomes. The book starts with a presentation
of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity,
state dependence, selectivity, endogeneity, underreporting, and clustered sampling.
Testing and estimation is discussed from frequentist and Bayesian perspectives. Finally,
applications are reviewed in fields such as economics, marketing, sociology, demography,
and health sciences. The fifth edition contains several new topics, including copula
functions, Poisson regression for non-counts, additional semi-parametric methods,
and discrete factor models. Other sections have been reorganized, rewritten, and extended
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