Graduate students and researchers are provided 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 fourth edition contains several new sections, for example on
nonnested hurdle models, quantile regression and on software. Many other sections have
been entirely rewritten and extended.
Written for: Researchers, graduate students
Keywords:
Count process
Selektivität
Zehldaten
Zeitreihenanalyse
count process
maximum likelihood
over dispersion
poisson regression
sample selection
Ökonometrie
Table of contents
Introduction.
Probability Models for Count
Data.
Econometric Modeling
Basic Issues.
Econometric Modeling
Extensions.
Correlated Count Data.
Bayesian Analysis of Count
Variables.
Applications
420 pages
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