Regression Analysis of Count Data
Authors:
|
A. Colin Cameron and Pravin K. Trivedi |
| Publisher: |
Cambridge University Press |
| Copyright: |
1998 |
| ISBN-13: |
978-0-521-63567-7 |
| Pages: |
475; paperback |
| Price: |
$39.50 |
|
|
|
|
Comment from the Stata technical group
Cameron and Trivedi’s Regression Analysis of Count Data gives the
reader an impressive survey of the statistical and econometric literature on
count data regressions. For the uninitiated, this book provides a thorough
introduction to modeling count data, but it would serve equally well as a
desk reference for the statistical professional.
Table of contents
List of Figures
List of Tables
Preface
1 Introduction
1.1 Poisson Distribution
1.2 Poisson Regression
1.3 Examples
1.4 Overview of Major Issues
1.5 Bibliographic Notes
2 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 Bibliographic Notes
2.9 Exercises
3 Basic Count Regression
3.1 Introduction
3.2 Poisson MLE, PLME, 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-Choise Models
3.7 Other Models
3.8 Iteratively Reweighted Least Squares
3.9 Bibliographic Notes
3.10 Exercises
4 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 Bibliographic Notes
4.12 Exercises
5 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 Bibliographic Notes
5.10 Exercises
6 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 Bibliographic Notes
6.8 Exercises
7 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
7.11 Derivations: Tests of Serial Correlation
7.12 Bibliographic Notes
7.13 Exercises
8 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 Bibliographic Notes
9 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 Bibliographic Notes
9.10 Exercises
10 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 Bibliographic Notes
10.8 Exercises
11 Nonrandom Samples and Simultaneity
11.1 Introduction
11.2 Alternative Sampling Frames
11.3 Simultaneity
11.4 Sample Selection
11.5 Bibliographic Notes
12 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
12.5 Flexible Models of Conditional Variance
12.6 Example and Model Comparison
12.7 Derivations
12.8 Count Models: Retrospect and Prospect
12.9 Bibliographic Notes
Appendices:
A Notation and Acronyms
B Functions, Distributions, and Moments
B.1 Gamma Function
B.2 Some Distributions
B.3 Moments of Truncated Poisson
C Software
References
Author Index
Subject Index
|
){kind=link}
){kind=link}