Regression Models for Categorical Dependent Variables Using Stata

TLDR: In this article, the authors present a brief tutorial for estimating, testing, fit, and interpretation of ordinal and binary outcomes using Stata. But they do not discuss how to apply these models to other estimation commands, such as post-estimation analysis.

Abstract: Preface PART I GENERAL INFORMATION Introduction What is this book about? Which models are considered? Whom is this book for? How is the book organized? What software do you need? Where can I learn more about the models? Introduction to Stata The Stata interface Abbreviations How to get help The working directory Stata file types Saving output to log files Using and saving datasets Size limitations on datasets Do-files Using Stata for serious data analysis Syntax of Stata commands Managing data Creating new variables Labeling variables and values Global and local macros Graphics A brief tutorial Estimation, Testing, Fit, and Interpretation Estimation Postestimation analysis Testing estat command Measures of fit Interpretation Confidence intervals for prediction Next steps PART II MODELS FOR SPECIFIC KINDS OF OUTCOMES Models for Binary Outcomes The statistical model Estimation using logit and probit Hypothesis testing with test and lrtest Residuals and influence using predict Measuring fit Interpretation using predicted values Interpretation using odds ratios with listcoef Other commands for binary outcomes Models for Ordinal Outcomes The statistical model Estimation using ologit and oprobit Hypothesis testing with test and lrtest Scalar measures of fit using fitstat Converting to a different parameterization The parallel regression assumption Residuals and outliers using predict Interpretation Less common models for ordinal outcomes Models for Nominal Outcomes with Case-Specific Data The multinomial logit model Estimation using mlogit Hypothesis testing of coefficients Independence of irrelevant alternatives Measures of fit Interpretation Multinomial probit model with IIA Stereotype logistic regression Models for Nominal Outcomes with Alternative-Specific Data Alternative-specific data organization The conditional logit model Alternative-specific multinomial probit The sturctural covariance matrix Rank-ordered logistic regression Conclusions Models for Count Outcomes The Poisson distribution The Poisson regression model The negative binomial regression model Models for truncated counts The hurdle regression model Zero-inflated count models Comparisons among count models Using countfit to compare count models More Topics Ordinal and nominal independent variables Interactions Nonlinear models Using praccum and forvalues to plot predictions Extending SPost to other estimation commands Using Stata more efficiently Conclusions Appendix A Syntax for SPost Commands Appendix B Description of Datasets References Author Index Subject Index