Microeconometrics Using Stata, 2nd 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 famil...

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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.

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 c...

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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.

1 Stata basics
1.1 Interactive use
1.2 Documentation
1.3 Command syntax and operators
1.4 Do-files and log files
1.5 Scalars and matrices
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1 Stata basics
1.1 Interactive use
1.2 Documentation
1.3 Command syntax and operators
1.4 Do-files and log files
1.5 Scalars and matrices
1.6 Using results from Stata commands
1.7 Global and local macros
1.8 Looping commands
1.9 Mata and Python in Stata
1.10 Some useful commands
1.11 Template do-file
1.12 Community-contributed commands
1.13 Additional resources
1.14 Exercises
2 Data management and graphics
2.1 Introduction
2.2 Types of data
2.3 Inputting data
2.4 Data management
2.5 Manipulating datasets
2.6 Graphical display of data
2.7 Additional resources
2.8 Exercises
3 Linear regression basics
3.1 Introduction
3.2 Data and data summary
3.3 Transformation of data before regression
3.4 Linear regression
3.5 Basic regression analysis
3.6 Specification analysis
3.7 Specification tests
3.8 Sampling weights
3.9 OLS using Mata
3.10 Additional resources
3.11 Exercises
4 Linear regression extensions
4.1 Introduction
4.2 In-sample prediction
4.3 Out-of-sample prediction
4.4 Predictive margins
4.5 Marginal effects
4.6 Regression decomposition analysis
4.7 Shapley decomposition of relative regressor importance
4.8 Differences-in-differences estimators
4.9 Additional resources
4.10 Exercises
5 Simulation
5.1 Introduction
5.2 Pseudorandom-number generators
5.3 Distribution of the sample mean
5.4 Pseudorandom-number generators: Further details
5.5 Computing integrals
5.6 Simulation for regression: Introduction
5.7 Additional resources
5.8 Exercises
6 Linear regression with correlated errors
6.1 Introduction
6.2 Generalized least-squares and FGLS regression
6.3 Modeling heteroskedastic data
6.4 OLS for clustered data
6.5 FGLS estimators for clustered data
6.6 Fixed-effects estimator for clustered data
6.7 Linear mixed models for clustered data
6.8 Systems of linear regressions
6.9 Survey data: Weighting, clustering, and stratification
6.10 Additional resources
6.11 Exercises
7 Linear instrumental-variables regression
7.1 Introduction
7.2 Simultaneous equations model
7.3 Instrumental-variables estimation
7.4 Instrumental-variables example
7.5 Weak instruments
7.6 Diagnostics and tests for weak instruments
7.7 Inference with weak instruments
7.8 Finite sample inference with weak instruments
7.9 Other estimators
7.10 Three-stage least-squares systems estimation
7.11 Additional resources
7.12 Exercises
8 Linear panel-data models: Basics
8.1 Introduction
8.2 Panel-data methods overview
8.3 Summary of panel data
8.4 Pooled or population-averaged estimators
8.5 Fixed-effects or within estimator
8.6 Between estimator
8.7 Random-effects estimator
8.8 Comparison of estimators
8.9 First-difference estimator
8.10 Panel-data management
8.11 Additional resources
8.12 Exercises
9 Linear panel-data models: Extensions
9.1 Introduction
9.2 Panel IV estimation
9.3 Hausman–Taylor estimator
9.4 Arellano–Bond estimator
9.5 Long panels
9.6 Additional resources
9.7 Exercises
10 Introduction to nonlinear regression
10.1 Introduction
10.2 Binary outcome models
10.3 Probit model
10.4 MEs and coefficient interpretation
10.5 Logit model
10.6 Nonlinear least squares
10.7 Other nonlinear estimators
10.8 Additional resources
10.9 Exercises
11 Tests of hypotheses and model specification
11.1 Introduction
11.2 Critical values and p-values
11.3 Wald tests and confidence intervals
11.4 Likelihood-ratio tests
11.5 Lagrange multiplier test (or score test)
11.6 Multiple testing
11.7 Test size and power
11.8 The power onemean command for multiple regression
11.9 Specification tests
11.10 Permutation tests and randomization tests
11.11 Additional resources
11.12 Exercises
12 Bootstrap methods
12.1 Introduction
12.2 Bootstrap methods
12.3 Bootstrap pairs using the vce(bootstrap) option
12.4 Bootstrap pairs using the bootstrap command
12.5 Percentile-t bootstraps with asymptotic refinement
12.6 Wild bootstrap with asymptotic refinement
12.7 Bootstrap pairs using bsample and simulate
12.8 Alternative resampling schemes
12.9 The jackknife
12.10 Additional resources
12.11 Exercises
13 Nonlinear regression methods
13.1 Introduction
13.2 Nonlinear example: Doctor visits
13.3 Nonlinear regression methods
13.4 Different estimates of the VCE
13.5 Prediction
13.6 Predictive margins
13.7 Marginal effects
13.8 Model diagnostics
13.9 Clustered data
13.10 Additional resources
13.11 Exercises
14 Flexible regression: Finite mixtures and nonparametric
14.1 Introduction
14.2 Models based on finite mixtures
14.3 FMM example: Earnings of doctors
14.4 Global polynomials
14.5 Regression splines
14.6 Nonparametric regression
14.7 Partially parametric regression
14.8 Additional resources
14.9 Exercises
15 Quantile regression
15.1 Introduction
15.2 Conditional quantile regression
15.3 CQR for medical expenditures data
15.4 CQR for generated heteroskedastic data
15.5 Quantile treatment effects for a binary treatment
15.6 Additional resources
15.7 Exercises
A Programming in Stata
A.1 Stata matrix commands
A.2 Programs
A.3 Program debugging
A.4 Additional resources
B Mata
B.1 How to run Mata
B.2 Mata matrix commands
B.3 Programming in Mata
B.4 Additional resources
C Optimization in Mata
C.1 Mata moptimize() function
C.2 Mata optimize() function
C.3 Additional resources
Glossary of abbreviations
References
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