Regression Analysis of Count Data

Transcription

1 Regression Analysis of Count Data A. Colin Cameron Pravin K. Trivedi Hfl CAMBRIDGE UNIVERSITY PRESS

2 List offigures List oftables Preface Introduction 1.1 Poisson Distribution 1.2 Poisson Regression 1.3 Examples 1.4 Overview of Major Issues 1.5 Bibliographie Notes Model Specification and Estimation 2.1 Introduction 2.2 Example and Definitions 2.3 Likelihood-Based Models 2.4 Generalized Linear Models 2.5 Moment-Based Models 2.6 Testing 2.7 Derivations 2.8 Bibliographie Notes 2.9 Exercises Basic Count Regression 3.1 Introduction 3.2 Poisson MLE, PMLE, and GLM 3.3 Negative binomial MLE and QGPMLE 3.4 Overdispersion Tests 3.5 Use of Regression Results 3.6 Ordered and Other Discrete-Choice Models 3.7 Other Models 3.8 Iteratively Reweighted Least Squares 3.9 Bibliographie Notes 3.10 Exercises page xi xii XV

3 viii Generalized Count Regression 4.1 Introduction 4.2 Mixture Models for Unobserved Heterogeneity 4.3 Models Based on Waiting-Time Distributions 4.4 Katz, Double-Poisson, and Generalized Poisson 4.5 Truncated Counts 4.6 Censored Counts 4.7 Hurdle and Zero-Inflated Models 4.8 Finite Mixtures and Latent Class Analysis 4.9 Estimation by Simulation 4.10 Derivations 4.11 Bibliographie Notes 4.12 Exercises Model Evaluation and Testing 5.1 Introduction 5.2 Residual Analysis 5.3 Goodness of Fit 5.4 Hypothesis Tests 5.5 Inference with Finite Sample Corrections 5.6 Conditional Moment Specification Tests 5.7 Discriminating among Nonnested Models 5.8 Derivations 5.9 Bibliographie Notes 5.10 Exercises Empirical Illustrations 6.1 Introduction 6.2 Background 6.3 Analysis of Demand for Health Services 6.4 Analysis of Recreational Trips 6.5 LR Test: A Digression 6.6 Concluding Remarks 6.7 Bibliographie Notes 6.8 Exercises Time Series Data 7.1 Introduction 7.2 Models for Time Series Data 7.3 Static Regression 7.4 Integer-Valued ARMA Models 7.5 Autoregressive Models 7.6 Serially Correlated Error Models 7.7 State-Space Models 7.8 Hidden Markov Models 7.9 Discrete ARMA Models 7.10 Application

4 ix Derivations: Tests of Serial Correlation Bibliographie Notes Exercises Multivariate Data 8.1 Introduction 8.2 Characterizing Dependence 8.3 Parametric Models 8.4 Moment-Based Estimation 8.5 Orthogonal Polynomial Series Expansions 8.6 Mixed Multivariate Models 8.7 Derivations 8.8 Bibliographie Notes Longitudinal Data 9.1 Introduction 9.2 Models for Longitudinal Data 9.3 Fixed Effects Models 9.4 Random Effects Models 9.5 Discussion 9.6 Specification Tests 9.7 Dynamic and Transition Models 9.8 Derivations 9.9 Bibliographie Notes 9.10 Exercises Measurement Errors 10.1 Introduction 10.2 Measurement Errors in Exposure 10.3 Measurement Errors in Regressors 10.4 Measurement Errors in Counts 10.5 Underreported Counts 10.6 Derivations 10.7 Bibliographie Notes 10.8 Exercises Nonrandom Samples and Simultaneity 11.1 Introduction 11.2 Alternative Sampling Frames 11.3 Simultaneity 11.4 Sample Selection 11.5 Bibliographie Notes Flexible Methods for Counts 12.1 Introduction 12.2 Efficient Moment-Based Estimation 12.3 Flexible Distributions Using Series Expansions 12.4 Flexible Models of Conditional Mean

5 12.5 Flexible Models of Conditional Varianee Example and Model Comparison Derivations Count Models: Retrospect and Prospect Bibliographie Notes 369 Appendices: Notation and Acronyms Functions, Distributions, and Moments B. 1 Gamma Function B.2 Some Distributions B.3 Moments of Truncated Poisson Software References Author Index Subject Index ,