**Microeconometrics Using Stata, Second Edition**

228,500원

## Volume I: Cross-Sectional and Panel Regression Methods

## Volume II: Nonlinear Models and Causal Inference Methods

Authors: A. Colin Cameron and Pravin K. Trivedi Publisher: Stata Press Copyright: 2022 ISBN-13: 978-1-59718-359-8 Pages: 1,675; paperback 이전 초판 한글 번역서 구입 가능## Volume I: Cross-Sectional and Panel Regression Methods

ISBN-13: 978-1-59718-361-1 Pages: 817; paperback

## Volume II: Nonlinear Models and Causal Inference Methods

ISBN-13: 978-1-59718-362-8 Pages: 858; paperback

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 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*.

1.2 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

2.2 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

3.2 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

4.2 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

5.2 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

6.2 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

7.2 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

8.2 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

9.2 Panel IV estimation

9.3 Hausman–Taylor estimator

9.4 Arellano–Bond estimator

9.5 Long panels

9.6 Additional resources

9.7 Exercises

10.2 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

11.2 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

12.2 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

13.2 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

14.2 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

15.2 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.2 Programs

A.3 Program debugging

A.4 Additional resources

B.2 Mata matrix commands

B.3 Programming in Mata

B.4 Additional resources

C.2 Mata optimize() function

C.3 Additional resources

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

17.2 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

18.2 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

19.2 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

20.2 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

21.2 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

22.2 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

23.2 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

24.2 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

25.2 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

26.2 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

27.2 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

28.2 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

29.2 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

30.2 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