Econometric Analysis of Count Data
282 pages, parution le 01/07/2000
(3eme édition)
Résumé
The book provides graduate students and researchers with an
up-to-date survey of econometric and statistical techniques
for the analysis of count data, with a focus on regression
models. Specialised discrete data probability models are
required in order to interpret results in terms of an
underlying structural process, and to use the model for
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. Classical and Bayesian methods of
estimation and inference are presented. Finally,
applications are reviewed in fields such as economics,
marketing, sociology, demography, and health sciences.
Probability Models for Count Data
Econometric Modeling - Basic Issues.
Econometric Modeling - Extensions.
Correlated Count Data.
Bayesian Analysis of Count Variables.
Applications.
Contents :
Introduction.
- Poisson Regression Model
- Some Thoughts on Methodology.
- Some Thoughts on Methodology.
- Examples.
- Organization of the Book.
Probability Models for Count Data
- Introduction.
- Poisson Distribution.
- Further Distributions for Count Data.
- Modified Count Data Distributions.
- Generaliziations.
- Duration Analysis and Count Data.
Econometric Modeling - Basic Issues.
- Poisson Regression Model.
- Estimation.
- Sources of Misspecification.
- Testing for Misspecification.
Econometric Modeling - Extensions.
- Introduction.
- Unobserved Heterogeneity.
- Dependent Count Process.
- Exogenous Selectivity.
- Endogenous Selectivity.
- Endogenous Regressors.
- Generalized Variance Models.
Correlated Count Data.
- Multivariate Count Data.
- Panel Data Models.
- Time-Series Count Data Models.
Bayesian Analysis of Count Variables.
- Bayesian Analysis of the Poisson Model.
- A Poisson Model with Underreporting.
- Estimation of the Multivariate Poisson-log-normal Model
by MCMC.
- Estimation of a Random Coefficients Model by
MCMC.
Applications.
- Accidents.
- Health Economics.
- Demography.
- Marketing and Management.
- Labor Mobility.
L'auteur - Rainer Winkelmann
University of Zürich, Switzerland
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Springer |
| Auteur(s) | Rainer Winkelmann |
| Parution | 01/07/2000 |
| Édition | 3eme édition |
| Nb. de pages | 282 |
| Format | 15,5 x 24 |
| Couverture | Relié |
| Poids | 566g |
| Intérieur | Noir et Blanc |
| EAN13 | 9783540673408 |
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