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Econometric analysis of count data

Author: Rainer Winkelmann
Publisher: Berlin : Springer, 2008.
Edition/Format:   Print book : English : 5th edView all editions and formats
Summary:

The book provides an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Alternative models address unobserved  Read more...

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Rainer Winkelmann
ISBN: 9783540776482 3540776486 9783540783893 354078389X
OCLC Number: 209773239
Description: xiv, 333 pages : illustrations ; 25 cm
Contents: Cover --
Preface --
Contents --
1 Introduction --
1.1 Poisson Regression Model --
1.2 Examples --
1.3 Organization of the Book --
2 Probability Models for Count Data --
2.1 Introduction --
2.2 Poisson Distribution --
2.2.1 Definitions and Properties --
2.2.2 Genesis of the Poisson Distribution --
2.2.3 Poisson Process --
2.2.4 Generalizations of the Poisson Process --
2.2.5 Poisson Distribution as a Binomial Limit --
2.2.6 Exponential Interarrival Times --
2.2.7 Non-Poissonness --
2.3 Further Distributions for Count Data --
2.3.1 Negative Binomial Distribution --
2.3.2 Binomial Distribution --
2.3.3 Logarithmic Distribution --
2.3.4 Summary --
2.4 Modified Count Data Distributions --
2.4.1 Truncation --
2.4.2 Censoring and Grouping --
2.4.3 Altered Distributions --
2.5 Generalizations --
2.5.1 Mixture Distributions --
2.5.2 Compound Distributions --
2.5.3 Birth Process Generalizations --
2.5.4 Katz Family of Distributions --
2.5.5 Additive Log-Differenced Probability Models --
2.5.6 Linear Exponential Families --
2.5.7 Summary --
2.6 Distributions for Over- and Underdispersion --
2.6.1 Generalized Event Count Model --
2.6.2 Generalized Poisson Distribution --
2.6.3 Poisson Polynomial Distribution --
2.6.4 Double Poisson Distribution --
2.6.5 Summary --
2.7 Duration Analysis and Count Data --
2.7.1 Distributions for Interarrival Times --
2.7.2 Renewal Processes --
2.7.3 Gamma Count Distribution --
2.7.4 Duration Mixture Models --
3 Poisson Regression --
3.1 Specification --
3.1.1 Introduction --
3.1.2 Assumptions of the Poisson Regression Model --
3.1.3 Ordinary Least Squares and Other Alternatives --
3.1.4 Interpretation of Parameters --
3.1.5 Period at Risk --
3.2 Maximum Likelihood Estimation --
3.2.1 Introduction --
3.2.2 Likelihood Function and Maximization --
3.2.3 Newton-Raphson Algorithm --
3.2.4 Properties of the Maximum Likelihood Estimator --
3.2.5 Estimation of the Variance Matrix --
3.2.6 Approximate Distribution of the Poisson Regression Coefficients --
3.2.7 Bias Reduction Techniques --
3.3 Pseudo-Maximum Likelihood --
3.3.1 Linear Exponential Families --
3.3.2 Biased Poisson Maximum Likelihood Inference --
3.3.3 Robust Poisson Regression --
3.3.4 Non-Parametric Variance Estimation --
3.3.5 Poisson Regression and Log-Linear Models --
3.3.6 Generalized Method of Moments --
3.4 Sources of Misspecification --
3.4.1 Mean Function --
3.4.2 Unobserved Heterogeneity --
3.4.3 Measurement Error --
3.4.4 Dependent Process --
3.4.5 Selectivity --
3.4.6 Simultaneity and Endogeneity --
3.4.7 Underreporting --
3.4.8 Excess Zeros --
3.4.9 Variance Function --
3.5 Testing for Misspecification --
3.5.1 Classical Specification Tests --
3.5.2 Regression Based Tests --
3.5.3 Goodness-of-Fit Tests --T.
Responsibility: Rainer Winkelmann.

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From the reviews:JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION"Winkleman has published numerous articles on using content models in economics and other social science journals. Because these are Read more...

 
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