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Regression Models for Categorical and Limited Dependent Variables
Comment from the Stata technical groupRegression Models for Categorical and Limited Dependent Variables, by J. Scott Long of Indiana University, is accessible to students and professionals alike. The author provides a unified treatment of the most prevalent and useful models for categorical and limited dependent variables. The book places a strong emphasis on model interpretation that is not found in most statistics texts. The mathematics is thorough but is complementary rather than focal. This is an excellent resource for an introduction to the categorical and limited dependent variables or as a desk reference. For Stata users, the book benefits greatly from the associated software. Dr. Long has developed and made available a suite of Stata commands to implement many of the techniques discussed in the book and programs to do the analyses that are contained in his book. The commands and programs can be accessed by pointing your web browser to http://www.indiana.edu/~jslsoc/spost_stata6.htm. They can also be installed directly in Stata with just a few net commands. Table of contentsList of Figures
List of Tables
Series Editor’s Introduction
Abbreviations and Notation
1 Introduction
1.1. Linear and Nonlinear Models
1.2. Organization 1.3. Orientation 1.4. Bibliographic Notes 2 Continuous Outcomes: The Linear Regression Model
2.1. The Linear Regression Model
2.2. Interpreting Regression Coefficients 2.3. Estimation by Ordinary Least Squares 2.4. Nonlinear Linear Regression Models 2.5. Violations of the Assumptions 2.6. Maximum Likelihood Estimation 2.7. Conclusions 2.8. Bibliographic Notes 3 Binary Outcomes: The Linear Probability, Probit, and Logit Models
3.1. The Linear Probability Model
3.2. A Latent Variable Model for Binary Variables 3.3. Identification 3.4. A Nonlinear Probability Model 3.5. ML Estimation 3.6. Numerical Methods for ML Estimation 3.7. Interpretation 3.8. Interpretation Using Odds Ratios 3.9. Conclusions 3.10. Bibliographic Notes 4 Hypothesis Testing and Goodness of Fit
4.1. Hypothesis Testing
4.2. Residuals and Influence 4.3. Scalar Measures of Fit 4.4. Conclusions 4.5. Bibliographic Notes 5 Ordinal Outcomes: Ordered Logit and Ordered Probit Analysis
5.1. A Latent Variable Model for Ordinal Variables
5.2. Identification 5.3. Estimation 5.4. Interpretation 5.5. The Parallel Regression Assumption 5.6. Related Models for Ordinal Data 5.7. Conclusions 5.8. Bibliographic Notes 6 Nominal Outcomes: Multinomial Logit and Related Models
6.1. Introduction to the Multinomial Logit Model
6.2. The Multinomial Logit Model 6.3. ML Estimation 6.4. Computing and Testing Other Contrasts 6.5. Two Useful Tests 6.6. Interpretation 6.7. The Conditional Logit Model 6.8. Independence of Irrelevant Alternatives 6.9. Related Models 6.10. Conclusions 6.11. Bibliographic Notes 7 Limited Outcomes: The Tobit Model
7.1. The Problem of Censoring
7.2. Truncated and Censored Distributions 7.3. The Tobit Model for Censored Outcomes 7.4. Estimation 7.5. Interpretation 7.6. Extensions 7.7. Conclusions 7.8. Bibliographic Notes 8 Count Outcomes: Regression Models for Counts
8.1. The Poisson Distribution
8.2. The Poisson Regression Model 8.3. The Negative Binomial Regression Model 8.4. Models for Truncated Counts 8.5. Zero Modified Counted Models 8.6. Comparisons Among Count Models 8.7. Conclusions 8.8. Bibliographic Notes 9 Conclusions
9.1. Links Using Latent Variable Models
9.2. The Generalized Linear Model 9.3. Similarities Among Probability Models 9.4. Event History Analysis 9.5. Log-Linear Models A Answers to Exercises
References
Author Index
Subject Index
About the Author
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