Microeconometrics Using Stata Revised Edition

A complete and up-to-date survey of microeconometric methods available in Stata, Microeconometrics Using Stata, Revised Edition is an outstanding introduction to microeconometrics and how to execute microeconometric research using Stata. It covers topics left out of most microeconometrics textbooks and omitted from basic introductions to Stata. This revised edition has been updated to reflect the new features available in Stata 11 that are useful to microeconomists. Instead of using mfx and the user-written margeff commands, the authors employ the new margins command, emphasizing both marginal effects at the means and average marginal effects. They also replace the xi command with factor variables, which allow you to specify indicator variables and interaction effects. Along with several new examples, this edition presents the new gmm command for generalized method of moments and nonlinear instrumental-variables estimation. In addition, the chapter on maximum likelihood estimation incorporates enhancements made to ml in Stata 11. Throughout the book, the authors use simulation methods to illustrate features of the estimators and tests described and provide an in-depth Stata example for each topic discussed. They also show how to use Stata’s programming features to implement methods for which Stata does not have a specific command. The unique combination of topics, intuitive introductions to methods, and detailed illustrations of Stata examples make this book an invaluable, hands-on addition to the library of anyone who uses microeconometric methods.

Stata BasicsInteractive use DocumentationCommand syntax and operatorsDo-files and log filesScalars and matricesUsing results from Stata commandsGlobal and local macrosLooping commandsSome useful commandsTemplate do-fileUser-written commands Data Management and Graphics IntroductionTypes of dataInputting dataData managementManipulating datasets Graphical display of data Linear Regression Basics Introduction Data and data summary Regression in levels and logs Basic regression analysis Specification analysis Prediction Sampling weights OLS using Mata Simulation Introduction Pseudorandom-number generators: Introduction Distribution of the sample meanPseudorandom-number generators: Further details Computing integrals Simulation for regression: Introduction GLS RegressionIntroduction GLS and FGLS regression Modeling heteroskedastic data System of linear regressions Survey data: Weighting, clustering, and stratification Linear Instrumental-Variables Regression Introduction IV estimation IV example Weak instruments Better inference with weak instruments 3SLS systems estimation Quantile Regression Introduction QR QR for medical expenditures data QR for generated heteroskedastic data QR for count data Linear Panel-Data Models: Basics Introduction Panel-data methods overview Panel-data summary Pooled or population-averaged estimators Within estimator Between estimator RE estimator Comparison of estimators First-difference estimator Long panels Panel-data management Linear Panel-Data Models: Extensions Introduction Panel IV estimation Hausman–Taylor estimator Arellano–Bond estimator Mixed linear models Clustered data Nonlinear Regression Methods Introduction Nonlinear example: Doctor visits Nonlinear regression methods Different estimates of the VCE Prediction Marginal effects Model diagnostics Nonlinear Optimization Methods Introduction Newton–Raphson method Gradient methods The ml command: lf method Checking the program The ml command: d0, d1, d2, lf0, lf1, and lf2 methods The Mata optimize() function Generalized method of moments Testing Methods Introduction Critical values and p-values Wald tests and confidence intervals Likelihood-ratio tests Lagrange multiplier test (or score test) Test size and power Specification tests Bootstrap Methods IntroductionBootstrap methods Bootstrap pairs using the vce(bootstrap) option Bootstrap pairs using the bootstrap command Bootstraps with asymptotic refinement Bootstrap pairs using bsample and simulate Alternative resampling schemes The jackknife Binary Outcome ModelsIntroductionSome parametric modelsEstimation Example Hypothesis and specification tests Goodness of fit and prediction Marginal effects Endogenous regressors Grouped data Multinomial Models IntroductionMultinomial models overview Multinomial example: Choice of fishing mode Multinomial logit model Conditional logit model Nested logit model Multinomial probit model Random-parameters logit Ordered outcome models Multivariate outcomes Tobit and Selection ModelsIntroductionTobit modelTobit model exampleTobit for lognormal data Two-part model in logs Selection model Prediction from models with outcome in logs Count-Data ModelsIntroduction Features of count data Empirical example 1 Empirical example 2 Models with endogenous regressors Nonlinear Panel ModelsIntroductionNonlinear panel-data overview Nonlinear panel-data example Binary outcome models Tobit model Count-data models Appendix A: Programming in Stata Appendix B: Mata Glossary References Author Index Subject Index Stata resources and Exercises appear at the end of each chapter.

Revised Edition