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Econometric analysis of count data

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...

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Main Author: Winkelmann, Rainer.
Other Authors: SpringerLink (Online service)
Format: eBook
Language: English
Published: Berlin : Springer, 2008.
Berlin : 2008.
Physical Description: 1 online resource (xiv, 333 pages) : illustrations.
Edition: 5th ed.
Subjects:
Econometrics.
Time-series analysis.
Labor mobility -- Econometric models.
Table of 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
  • 2̂.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
  • 3̂.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.

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