Regression Models for Categorical and Limited Dependent VariablesA unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how models relate to linear regression models whenever possible. |
Contents
Introduction | 1 |
1 | 3 |
The Linear Regression Model | 11 |
4 | 28 |
The Linear Probability Probit and Logit | 34 |
3 | 43 |
8 | 52 |
Hypothesis Testing and Goodness of | 89 |
63 | 161 |
1 | 182 |
The Tobit Model | 187 |
28 | 191 |
41 | 200 |
Regression Models for Counts | 217 |
43 | 229 |
Conclusions | 251 |
4 | 108 |
Ordered Logit and Ordered Probit Analysis | 114 |
1 | 116 |
3 | 127 |
8 | 142 |
xi | 143 |
Multinomial Logit and Related Models | 148 |
A Answers to Exercises | 264 |
| 274 | |
| 280 | |
| 283 | |
| 296 | |
Common terms and phrases
analysis assume assumption B₁ binary logit censored Chapter chi-square computed consider constraints count models dependent discrete change dummy variables equal errors example exp(xẞ expected value factor change Figure fully standardized given hypothesis independent variables indicated intercept interpreted Labor Force Participation latent variable likelihood function linear regression model log likelihood log-linear models logit and probit logit model LR test M₁ M₂ marginal effect mean measures of fit ML estimator MNLM multinomial logit multinomial logit model NBRM nonlinear normal distribution odds ratio ordered logit ordered logit model ordered probit ordinal outcome overdispersion Panel parallel regression parameters plots Poisson distribution Poisson regression Poisson regression model Pr(y predicted probability prestige probability curve probit model sample slope ẞk standard deviation standardized coefficient statistic Table tobit truncated unit change Var(y variables constant versus Wald test x₁ xẞ y*-standardized z-test