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 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.

Volume I. Stata basics. Data management and graphics. Linear regression basics. Linear regression extensions. Simulation. Linear regression with correlated errors. Linear instrumental-variables regression. Linear panel-data models: Basics. Linear panel-data models: Extensions. Introduction to nonlinear regression. Tests of hypotheses and model specification. Bootstrap methods. Nonlinear regression methods. Flexible regression: Finite mixtures and nonparametric. Quantile regression. Programming in Stata. Mata. Optimization in Mata. Volume II. Nonlinear optimization methods. Binary outcome models. Multinomial models. Tobit and selection models. Count-data models. Survival analysis for duration data. Nonlinear panel models. Parametric models for heterogeneity and endogeneity. Randomized control trials and exogenous treatment effects. Endogenous treatment effects. Spatial regression. Semiparametric regression. Machine learning for prediction and inference. Bayesian methods: Basics. Bayesian methods: Markov chain Monte Carlo algorithms