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Econometric Analysis of Count Data

  • Rainer Winkelmann

Table of contents

  1. Front Matter
    Pages I-XV
  2. Rainer Winkelmann
    Pages 1-7
  3. Rainer Winkelmann
    Pages 9-64
  4. Rainer Winkelmann
    Pages 65-118
  5. Rainer Winkelmann
    Pages 119-169
  6. Rainer Winkelmann
    Pages 171-207
  7. Rainer Winkelmann
    Pages 209-219
  8. Rainer Winkelmann
    Pages 221-246
  9. Back Matter
    Pages 247-282

About this book

Introduction

The primary objective of this book is to provide an introduction to the econometric modeling of count data for graduate students and researchers. It should serve anyone whose interest lies either in developing the field fur­ ther, or in applying existing methods to empirical questions. Much of the material included in this book is not specific to economics, or to quantita­ tive social sciences more generally, but rather extends to disciplines such as biometrics and technometrics. Applications are as diverse as the number of congressional budget vetoes, the number of children in a household, and the number of mechanical defects in a production line. The unifying theme is a focus on regression models in which a dependent count variable is modeled as a function of independent variables which mayor may not be counts as well. The modeling of count data has come of age. Inclusion of some of the fundamental models in basic textbooks, and implementation on standard computer software programs bear witness to that. Based on the standard Poisson regression model, numerous extensions and alternatives have been developed to address the common challenges faced in empirical modeling (unobserved heterogeneity, selectivity, endogeneity, measurement error, and dependent observations in the context of panel data or multivariate data, to name but a few) as well as the challenges that are specific to count data (e. g. , over dispersion and underdispersion).

Keywords

Conditional Distribution Models Count Variables Econometric Modeling Internet calculus count data econometrics marketing panel data probability regression

Authors and affiliations

  • Rainer Winkelmann
    • 1
  1. 1.Institute for the Study of Labor (IZA)BonnGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-04149-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2000
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-662-04151-2
  • Online ISBN 978-3-662-04149-9
  • Buy this book on publisher's site