Any applied economic researcher using Stata and anyone 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 same explanation of 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.

This new edition covers all the new Stata developments relevant to microeconometrics that appeared since the the last edition in 2010. It also covers the most recent microeconometric methods that have been contributed by the Stata community but have not yet made it to Stata. For example, readers will find entire new chapters on treatment effects, duration models, spatial autoregressive models, lasso, and Bayesian analysis.

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.

This is just a brief overview of the contents of the book, but it exemplifies the breadth and ambition of the two volumes. In sum, it is an essential book for any applied researcher and advanced microeconometrics courses.

© Copyright 1996–2023 StataCorp LLC

**List of tables**

**List of figures**

**Preface to the Second Edition** (PDF)

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

© Copyright 1996–2023 StataCorp LLC