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