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)
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
© Copyright 1996–2023 StataCorp LLC
