Microeconometrics using Stata

Table of Contents 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.

Microeconometrics Using Stata, Revised Edition, by A. Colin Cameron and Pravin K. Trivedi, is an outstanding introduction to microeconometrics and how to do microeconometric research using Stata. Aimed at students and researchers, this book covers topics left out of microeconometrics textbooks and omitted from basic introductions to Stata. Cameron and Trivedi provide the most complete and up-to-date survey of microeconometric methods available in Stata. The revised edition has been updated to reflect the new features available in Stata 11 germane to microeconomists. Instead of using mfx and the community-contributed margeff commands, the revised edition uses the new margins command, emphasizing both marginal effects at the means and average marginal effects. Factor variables, which allow you to specify indicator variables and interaction effects, replace the xi command. The new gmm command for generalized method of moments and nonlinear instrumental-variables estimation is presented, along with several examples. Finally, the chapter on maximum likelihood estimation incorporates the enhancements made to ml in Stata 11. Early in the book, Cameron and Trivedi introduce simulation methods and then use them to illustrate features of the estimators and tests described in the rest of the book. While simulation methods are important tools for econometricians, they are not covered in standard textbooks. By introducing simulation methods, the authors arm students and researchers with techniques they can use in future work. Cameron and Trivedi address each topic with an in-depth Stata example, and they reference their 2005 textbook, Microeconometrics: Methods and Applications, where appropriate. The authors also show how to use Stata's programming features to implement methods for which Stata does not have a specific command. Although the book is not specifically about Stata programming, it does show how to solve many programming problems. These techniques are essential in applied microeconometrics because there will always be new, specialized methods beyond what has already been incorporated into a software package. Cameron and Trivedi's choice of topics perfectly reflects the current practice of modern microeconometrics. After introducing the reader to Stata, the authors introduce linear regression, simulation, and generalized least-squares methods. The section on cross-sectional techniques is thorough, with up-to-date treatments of instrumental-variables methods for linear models and of quantile-regression methods. The next section of the book covers estimators for the parameters of linear panel-data models. The authors' choice of topics is unique: after addressing the standard random-effects and fixed-effects methods, the authors also describe mixed linear models-a method used in many areas outside of econometrics. Cameron and Trivedi not only address methods for nonlinear regression models but also show how to code new nonlinear estimators in Stata. In addition to detailing nonlinear methods, which are omitted from most econometrics textbooks, this section shows researchers and students how to easily implement new nonlinear estimators. The authors next describe inference using analytical and bootstrap approximations to the distribution of test statistics. This section highlights Stata's power to easily obtain bootstrap approximations, and it also introduces the basic elements of statistical inference. Cameron and Trivedi then include an extensive section about methods for different nonlinear models. They begin by detailing methods for binary dependent variables. This section is followed by sections about multinomial models, tobit and selection models, count-data models, and nonlinear panel-data models. Two appendices about Stata programming complete the book. The unique combination of topics, intuitive introductions to methods, and detailed illustrations of Stata examples make Microeconometrics Using Stata an invaluable, hands-on addition to the library of anyone who uses microeconometric methods. (https://www.stata.com/bookstore/microeconometrics-stata/)