List of figures. I General information 1

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1 List of figures Preface xix xxi I General information 1 1 Introduction What is this book about? Which models are considered? Whom is this book for? How is the book organized? The SPost software Updating Stata Installing SPost Uninstalling SPost Installing SPost13 using search Installing SPost13 using net install Uninstalling SPost Sample do-files and datasets Installing the spost13 do package Using spex to load data and run examples Getting help with SPost What if an SPost command does not work? Getting help from the authors What we need to help you Where can I learn more about the models? Introduction to Stata 23

2 viii Contents 2.1 The Stata interface Abbreviations Getting help Online help PDF manuals Error messages Asking for help Other resources The working directory Stata file types Saving output to log files Using and saving datasets Data in Stata format Data in other formats Entering data by hand Size limitations on datasets Do-files Adding comments Long lines Stopping a do-file while it is running Creating do-files Recommended structure for do-files Using Stata for serious data analysis Syntax of Stata commands Commands Variable lists if and in qualifiers Options Managing data Looking at your data

3 ix Getting information about variables Missing values Selecting observations Selecting variables Creating new variables The generate command The replace command The recode command Labeling variables and values Variable labels Value labels The notes command Global and local macros Loops using foreach and forvalues Graphics The graph command A brief tutorial A do-file template Conclusion Estimation, testing, and fit Estimation Stata s output for ML estimation ML and sample size Problems in obtaining ML estimates Syntax of estimation commands Variable lists Using factor-variable notation in the variable list Specifying interaction and polynomials More on factor-variable notation Specifying the estimation sample

4 x Contents Missing data Information about missing values Postestimation commands and the estimation sample Weights and survey data Complex survey designs Options for regression models Robust standard errors Reading the estimation output Storing estimation results (Advanced) Saving estimates to a file Reformatting output with estimates table Testing One-tailed and two-tailed tests Wald and likelihood-ratio tests Wald tests with test and testparm LR tests with lrtest Avoiding invalid LR tests Measures of fit Syntax of fitstat Methods and formulas used by fitstat Example of fitstat estat postestimation commands Conclusion Methods of interpretation Comparing linear and nonlinear models Approaches to interpretation Method of interpretation based on predictions Method of interpretation using parameters Stata and SPost commands for interpretation Predictions for each observation

5 xi 4.4 Predictions at specified values Why use the m* commands instead of margins? Using margins for predictions Predictions using interaction and polynomial terms Making multiple predictions Predictions for groups defined by levels of categorical variables (Advanced) Nondefault predictions using margins The predict() option The expression() option Tables of predictions using mtable mtable with categorical and count outcomes (Advanced) Combining and formatting tables using mtable Marginal effects: Changes in predictions Marginal effects using margins Marginal effects using mtable Posting predictions and using mlincom Marginal effects using mchange Plotting predictions Plotting predictions with marginsplot Plotting predictions using mgen Interpretation of parameters The listcoef command Standardized coefficients Factor and percentage change coefficients Next steps II Models for specific kinds of outcomes Models for binary outcomes: Estimation, testing, and fit The statistical model A latent-variable model

6 xii Contents A nonlinear probability model Estimation using logit and probit commands Example of logit model Comparing logit and probit (Advanced) Observations predicted perfectly Hypothesis testing Testing individual coefficients Testing multiple coefficients Comparing LR and Wald tests Predicted probabilities, residuals, and influential observations Predicted probabilities using predict Residuals and influential observations using predict Least likely observations Measures of fit Information criteria Pseudo-R 2 s (Advanced) Hosmer Lemeshow statistic Other commands for binary outcomes Conclusion Models for binary outcomes: Interpretation Interpretation using regression coefficients Interpretation using odds ratios (Advanced) Interpretation using y* Marginal effects: Changes in probabilities Linked variables Summary measures of change MEMs and MERs AMEs Standard errors of marginal effects Should you use the AME, the MEM, or the MER?

7 xiii Examples of marginal effects AMEs for continuous variables AMEs for factor variables Summary table of AMEs Marginal effects for subgroups MEMs and MERs Marginal effects with powers and interactions The distribution of marginal effects (Advanced) Algorithm for computing the distribution of effects Ideal types Using local means with ideal types Comparing ideal types with statistical tests (Advanced) Using macros to test differences between ideal types Marginal effects for ideal types Tables of predicted probabilities Second differences comparing marginal effects Graphing predicted probabilities Using marginsplot Using mgen with the graph command Graphing multiple predictions Overlapping confidence intervals Adding power terms and plotting predictions (Advanced) Graphs with local means Conclusion Models for ordinal outcomes The statistical model A latent-variable model A nonlinear probability model Estimation using ologit and oprobit

8 xiv Contents Example of ordinal logit model Predicting perfectly Hypothesis testing Testing individual coefficients Testing multiple coefficients Measures of fit using fitstat (Advanced) Converting to a different parameterization The parallel regression assumption Testing the parallel regression assumption using oparallel Testing the parallel regression assumption using brant Caveat regarding the parallel regression assumption Overview of interpretation Interpreting transformed coefficients Marginal change in y Odds ratios Interpretations based on predicted probabilities Predicted probabilities with predict Marginal effects Plotting marginal effects Marginal effects for a quick overview Predicted probabilities for ideal types (Advanced) Testing differences between ideal types Tables of predicted probabilities Plotting predicted probabilities Probability plots and marginal effects Less common models for ordinal outcomes The stereotype logistic model The generalized ordered logit model (Advanced) Predictions without using factor-variable notation 374

9 xv The sequential logit model Conclusion Models for nominal outcomes The multinomial logit model Formal statement of the model Estimation using the mlogit command Weights and complex samples Options Example of MNLM Selecting different base outcomes Predicting perfectly Hypothesis testing mlogtest for tests of the MNLM Testing the effects of the independent variables Tests for combining alternatives Independence of irrelevant alternatives Hausman McFadden test of IIA Small Hsiao test of IIA Measures of fit Overview of interpretation Predicted probabilities with predict Marginal effects (Advanced) The distribution of marginal effects Tables of predicted probabilities (Advanced) Testing second differences (Advanced) Predictions using local means and subsamples Graphing predicted probabilities Odds ratios Listing odds ratios with listcoef Plotting odds ratios

10 xvi Contents 8.12 (Advanced) Additional models for nominal outcomes Stereotype logistic regression Conditional logit model Multinomial probit model with IIA Alternative-specific multinomial probit Rank-ordered logit model Conclusion Models for count outcomes The Poisson distribution Fitting the Poisson distribution with the poisson command Comparing observed and predicted counts with mgen The Poisson regression model Estimation using poisson Example of the PRM Factor and percentage changes in E(y x) Example of factor and percentage change Marginal effects on E(y x) Examples of marginal effects Interpretation using predicted probabilities Predicted probabilities using mtable and mchange Treating a count independent variable as a factor variable. 498 Predicted probabilities using mgen Comparing observed and predicted counts to evaluate model specification (Advanced) Exposure time The negative binomial regression model Estimation using nbreg NB1 and NB2 variance functions Example of NBRM Testing for overdispersion

11 xvii Comparing the PRM and NBRM using estimates table Robust standard errors Interpretation using E(y x) Interpretation using predicted probabilities Models for truncated counts Estimation using tpoisson and tnbreg Example of zero-truncated model Interpretation using E(y x) Predictions in the estimation sample Interpretation using predicted rates and probabilities (Advanced) The hurdle regression model Fitting the hurdle model Predictions in the sample Predictions at user-specified values Warning regarding sample specification Zero-inflated count models Estimation using zinb and zip Example of zero-inflated models Interpretation of coefficients Interpretation of predicted probabilities Predicted probabilities with mtable Plotting predicted probabilities with mgen Comparisons among count models Comparing mean probabilities Tests to compare count models Using countfit to compare count models Conclusion References 561 Author index 569 Subject index 573