This text is the most comprehensive work to date on microeconometrics, its methods and applications.

This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.

Ⅰ Preliminaries 1 Overview 2 Causal and Noncausal Models 3 Microeconomic Data Structures Ⅱ Core Methods 4 Linear Models 5 Maximum Likelihood and Nonlinear Least-Squares Estimation 6 Generalized Method of Moments and Systems Estimation 7 Hypothesis Tests 8 Specification Tests and Model Selection 9 Semiparametric Methods 10 Numerical Optimization Ⅲ Simulation-Based Methods 11 Bootstrap Methods 12 Simulation-Based Methods 13 Bayesian Methods Ⅳ Models for Cross-Section Data 14 Binary Outcome Models 15 Multinomial Models 16 Tobit and Selection Models 17 Transition Data: Survival Analysis 18 Mixture Models and Unobserved Heterogeneity 19 Models of Multiple Hazards 20 Models of Count Data Ⅴ Models for Panel Data 21 Linear Panel Models: Basics 22 Linear Panel Models: Extensions 23 Nonlinear Panel Models Ⅵ Further Topics 24 Stratified and Clustered Samples 25 Treatment Evaluation 26 Measurement Error Models 27 Missing Data and Imputation A Asymptotic Theory B Making Pseudo-Random Draws