Econometric Analysis of Count Data

Online-Ressource (digital)

Literaturverz. S. [299] - 319

Literaturverz. S. 299 - 319

Introduction; Probability Models for Count Data; Poisson Regression; Unobserved Heterogeneity; Sample Selection and Endogeneity; Zeros in Count Data Models; Correlated Count Data; Bayesian Analysis of Count Data; Applications

CoverPreface -- 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.

10.1007/978-3-540-78389-3 [DOI]

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