Presents an introduction to microeconometrics and how to execute microeconometric research using Stata. This title covers topics left out of most microeconometrics textbooks and omitted from basic introductions to Stata.

Stata Basics Interactive use Documentation Command syntax and operators Do-files and log files Scalars and matrices Using results from Stata commands Global and local macros Looping commands Some useful commands Template do-file User-written commands Data Management and Graphics Introduction Types of data Inputting data Data management Manipulating 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 mean Pseudorandom-number generators: Further details Computing integrals Simulation for regression: Introduction GLS Regression Introduction 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 Introduction Bootstrap 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 Models Introduction Some parametric models Estimation Example Hypothesis and specification tests Goodness of fit and prediction Marginal effects Endogenous regressors Grouped data Multinomial Models Introduction Multinomial 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 Models Introduction Tobit model Tobit model example Tobit for lognormal data Two-part model in logs Selection model Prediction from models with outcome in logs Count-Data Models Introduction Features of count data Empirical example 1 Empirical example 2 Models with endogenous regressors Nonlinear Panel Models Introduction Nonlinear 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.