A. Colin Cameron and Pravin K. Trivedi


 Two volumes
  1,675 pages
  Stata Press

              volume 1
Cover volume 2

This new edition, especially the second volume, includes many newer topics and methods that could have appeared in an updated edition of our 2005 book Microeconometrics: Methods and Applications. While the methods are implemented here using Stata, the material is more generally relevant to any microeconometrics researcher, regardless of the programming language used.

NOW AVAILABLE as Paperback, Ebook and Kindle
at https://www.stata-press.com/books/microeconometrics-stata/

and at Amazon

Volume 1: Cross-Sectional and Panel Regression Models
Volume 2: Nonlinear Models and Causal Inference Methods

The book includes coverage of Stata 17 (released April 2021).
The Stata book website includes links to download all datasets and programs used in the book.
The Stata book website link to Table of Contents at the end also has the author index and subject index.
For some related slides click here.

Sample Page from the eBook version through Vital Source

Detailed Table of Contents for 2022 Second Edition

The second edition covers over ten years of both developments in the methods most commonly-used in empirical microeconometrics analysis and enhancements to Stata. Our target user base consists of practitioners of applied microeconometrics, and the focus of the book remains the use of linear and nonlinear regression methods for cross-section and short panel data. Many of these methods are also used in related sciences, such as poltical science and sociology, and in biostatistics and epidemiology. We have attempted to not only update our previous coverage to bring it in line with newer tools in the latest edition (version 17) of Stata but also to bring into the book many topics and methods that are now actively studied and increasingly used in applied microeconometrics.

The new edition is much expanded (1,675 pages) and is split into two volumes.
To assist the reader we have provided numerous cross-references and a much lengthier subject index.
The first volume (chapters 1-15) focuses on the linear regression model as well as providing a brief introduction to nonlinear regression models.
This volume is an expanded version of chapters 1-10, and 12-13 and the appendices of the First and Revised Editions. In places there is greater explanation of underlying methods than in earlier editions, and much of the first volume is intended to be suitable for an advanced undergraduate course in addition to serving graduate students and researchers.

The second volume (chapters 16-30) covers the standard nonlinear models (chapters 11 and 14-18 of the earlier editions) as well as more advanced and more recent material. In addition to updated versions of existing chapters, the second volume includes new chapters on duration models, treatment effects in randomized control trials, treatment effects with endogenous treatments, parametric models for endogeneity and heterogeneity, spatial regression, semiparametric regression, machine learning and prediction, and Bayesian methods.

: Cross-Sectional and Panel Regression Models 
1.  Stata basics
2.  Data management and graphics
3.  Linear regression basics
4.  Linear regression extensions
5.  Simulation
6.  Linear regression with correlated errors
7.  Linear instrumental variables regression
8.  Linear panel-data models: Basics
9.  Linear panel-data models: Extensions
10. Introduction to nonlinear regression
11. Tests of hypotheses and model selection
12. Bootstrap methods
13. Nonlinear regression methods
14. Flexible regression: finite mixtures and nonparametric
15. Quantile regression
Appx.A: Programming in Stata
Appx.B: Mata
Appx.C: Optimization in Mata       

VOLUME 2: Nonlinear Models and Causal Inference Methods  
16. Nonlinear optimization methods
17. Binary outcome models
18. Multinomial models
19. Tobit and selection models
20. Count-data models
21. Survival analysis for duration data
22. Nonlinear panel models
23. Parametric models for heterogeneity and endogeneity
24. RCTs and exogenous treatment effects
25. Endogenous treatment effects
26. Spatial regression
27. Semiparametric regression
28. Machine learning for prediction and inference
29. Bayesian methods: Basics
30. Bayesian methods: MCMC algorithms 
Ex Comm Min 5/13/96
Ex Comm Min 5/13/96 For the earlier First Edition and Revised Edition see http://cameron.econ.ucdavis.edu/musbook/mus.html

A. Colin Cameron / University of California - Davis / http://cameron.econ.ucdavis.edu/
Pravin K. Trivedi / Indiana University - Bloomington / Emeritus