Microeconometrics Using Stata, Second Edition

Every applied economic researcher using Stata and everyone teaching or studying microeconometrics will benefit from Cameron and Trivedi's two volumes. They are an invaluable reference of the theory and intuition behind microeconometric methods using Stata. Those familiar with Cameron and Trivedi's Microeconometrics: Methods and Applications will find the same rigor. Those familiar with the previous edition of "Microeconometrics Using Stata" will find the familiar focus on Stata commands, their interpretation, and their connection with microeconometric theory as well as an introduction to computational concepts that should be part of any researcher's toolbox. And readers will find much more—so much more the second edition required a second volume. This new edition covers all the new Stata developments relevant to microeconometrics that appeared since the the last edition in 2010. For example, readers will find entire new chapters on treatment effects, duration models, spatial autoregressive models, lasso, and Bayesian analysis. But the authors didn't stop there. They also added discussions of the most recent microeconometric methods that have been contributed by the Stata community. The first volume introduces foundational microeconometric methods, including linear and nonlinear methods for cross-sectional data and linear panel data with and without endogeneity as well as overviews of hypothesis and model-specification tests. Beyond this, it teaches bootstrap and simulation methods, quantile regression, finite mixture models, and nonparametric regression. It also includes an introduction to basic Stata concepts and programming and to Mata for matrix programming and basic optimization. The second volume builds on methods introduced in the first volume and walks readers through a wide range of more advanced methods useful in economic research. It starts with an introduction to nonlinear optimization methods and then delves into binary outcome methods with and without endogeneity; tobit and selection model estimates with and without endogeneity; choice model estimation; count data with and without endogeneity for conditional means and count data for conditional quantiles; survival data; nonlinear panel-data methods with and without endogeneity; exogenous and endogenous treatment effects; spatial data modeling; semiparametric regression; lasso for prediction and inference; and Bayesian econometrics. With its encyclopedic coverage of modern econometric methods paired with many worked examples that demonstrate how to implement these methods in Stata, "Microeconometrics Using Stata, Second Edition" is a text that readers will come back to over and over for each new project or analysis they face. It is an essential reference for applied researchers and those taking microeconometrics courses.

16 Nonlinear optimization methods 16.1 Introduction 16.2 Newton–Raphson method 16.3 Gradient methods 16.4 Overview of ml, moptimize(), and optimize() 16.5 The ml command: lf method 16.6 Checking the program 16.7 The ml command: lf0–lf2, d0–d2, and gf0 methods 16.8 Nonlinear instrumental-variables (GMM) example 16.9 Additional resources 16.10 Exercises 17 Binary outcome models 17.1 Introduction 17.2 Some parametric models 17.3 Estimation 17.4 Example 17.5 Goodness of fit and prediction 17.6 Marginal effects 17.7 Clustered data 17.8 Additional models 17.9 Endogenous regressors 17.10 Grouped and aggregate data 17.11 Additional resources 17.12 Exercises 18 Multinomial models 18.1 Introduction 18.2 Multinomial models overview 18.3 Multinomial example: Choice of fishing mode 18.4 Multinomial logit model 18.5 Alternative-specific conditional logit model 18.6 Nested logit model 18.7 Multinomial probit model 18.8 Alternative-specific random-parameters logit 18.9 Ordered outcome models 18.10 Clustered data 18.11 Multivariate outcomes 18.12 Additional resources 18.13 Exercises 19 Tobit and selection models 19.1 Introduction 19.2 Tobit model 19.3 Tobit model example 19.4 Tobit for lognormal data 19.5 Two-part model in logs 19.6 Selection models 19.7 Nonnormal models of selection 19.8 Prediction from models with outcome in logs 19.9 Endogenous regressors 19.10 Missing data 19.11 Panel attrition 19.12 Additional resources 19.13 Exercises 20 Count-data models 20.1 Introduction 20.2 Modeling strategies for count data 20.3 Poisson and negative binomial models 20.4 Hurdle model 20.5 Finite-mixture models 20.6 Zero-inflated models 20.7 Endogenous regressors 20.8 Clustered data 20.9 Quantile regression for count data 20.10 Additional resources 20.11 Exercises 21 Survival analysis for duration data 21.1 Introduction 21.2 Data and data summary 21.3 Survivor and hazard functions 21.4 Semiparametric regression model 21.5 Fully parametric regression models 21.6 Multiple-records data 21.7 Discrete-time hazards logit model 21.8 Time-varying regressors 21.9 Clustered data 21.10 Additional resources 21.11 Exercises 22 Nonlinear panel models 22.1 Introduction 22.2 Nonlinear panel-data overview 22.3 Nonlinear panel-data example 22.4 Binary outcome and ordered outcome models 22.5 Tobit and interval-data models 22.6 Count-data models 22.7 Panel quantile regression 22.8 Endogenous regressors in nonlinear panel models 22.9 Additional resources 22.10 Exercises 23 Parametric models for heterogeneity and endogeneity 23.1 Introduction 23.2 Finite mixtures and unobserved heterogeneity 23.3 Empirical examples of FMMs 23.4 Nonlinear mixed-effects models 23.5 Structural equation models for linear structural equation models 23.6 Generalized structural equation models 23.7 ERM commands for endogeneity and selection 23.8 Additional resources 23.9 Exercises 24 Randomized control trials and exogenous treatment effects 24.1 Introduction 24.2 Potential outcomes 24.3 Randomized control trials 24.4 Regression in an RCT 24.5 Treatment evaluation with exogenous treatment 24.6 Treatment evaluation methods and estimators 24.7 Stata commands for treatment evaluation 24.8 Oregon Health Insurance Experiment example 24.9 Treatment-effect estimates using the OHIE data 24.10 Multilevel treatment effects 24.11 Conditional quantile TEs 24.12 Additional resources 24.13 Exercises 25 Endogenous treatment effects 25.1 Introduction 25.2 Parametric methods for endogenous treatment 25.3 ERM commands for endogenous treatment 25.4 ET commands for binary endogenous treatment 25.5 The LATE estimator for heterogeneous effects 25.6 Difference-in-differences and synthetic control 25.7 Regression discontinuity design 25.8 Conditional quantile regression with endogenous regressors 25.9 Unconditional quantiles 25.10 Additional resources 25.11 Exercises 26 Spatial regression 26.1 Introduction 26.2 Overview of spatial regression models 26.3 Geospatial data 26.4 The spatial weighting matrix 26.5 OLS regression and test for spatial correlation 26.6 Spatial dependence in the error 26.7 Spatial autocorrelation regression models 26.8 Spatial instrumental variables 26.9 Spatial panel-data models 26.10 Additional resources 26.11 Exercises 27 Semiparametric regression 27.1 Introduction 27.2 Kernel regression 27.3 Series regression 27.4 Nonparametric single regressor example 27.5 Nonparametric multiple regressor example 27.6 Partial linear model 27.7 Single-index model 27.8 Generalized additive models 27.9 Additional resources 27.10 Exercises 28 Machine learning for prediction and inference 28.1 Introduction 28.2 Measuring the predictive ability of a model 28.3 Shrinkage estimators 28.4 Prediction using lasso, ridge, and elasticnet 28.5 Dimension reduction 28.6 Machine learning methods for prediction 28.7 Prediction application 28.8 Machine learning for inference in partial linear model 28.9 Machine learning for inference in other models 28.10 Additional resources 28.11 Exercises 29 Bayesian methods: Basics 29.1 Introduction 29.2 Bayesian introductory example 29.3 Bayesian methods overview 29.4 An i.i.d. example 29.5 Linear regression 29.6 A linear regression example 29.7 Modifying the MH algorithm 29.8 RE model 29.9 Bayesian model selection 29.10 Bayesian prediction 29.11 Probit example 29.12 Additional resources 29.13 Exercises 30 Bayesian methods: Markov chain Monte Carlo algorithms 30.1 Introduction 30.2 User-provided log likelihood 30.3 MH algorithm in Mata 30.4 Data augmentation and the Gibbs sampler in Mata 30.5 Multiple imputation 30.6 Multiple-imputation example 30.7 Additional resources 30.8 Exercises Glossary of abbreviations References

Colin Cameron is a professor of economics at the University of California–Davis, where he teaches econometrics at undergraduate and graduate levels, as well as an undergraduate course in health economics. He has given short courses in Europe, Australia, Asia, and South America. His research interests are in microeconometrics, especially in robust inference for regression with clustered errors. He is currently an associate editor of the Stata Journal. Pravin K. Trivedi is a Distinguished Professor Emeritus at Indiana University–Bloomington and an honorary professor in the School of Economics at the University of Queensland. During his academic career, he has taught undergraduate- and graduate-level econometrics in the United States, England, Europe, and Australia. His research interests include microeconometrics and health economics. He has served as coeditor of the Econometrics Journal from 2000–2007 and associate editor of the Journal of Applied Econometrics from 1986–2015. He has coauthored (with David Zimmer) Copula Modeling in Econometrics: An Introduction for Practitioners (2007). Cameron and Trivedi’s joint work includes research articles on econometric models and tests for count data, the Econometric Society monograph Regression Analysis of Count Data, and the graduate-level text Microeconometrics: Methods and Applications.

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