Econometrics Guide

Preface

The book covers the core estimation and inference methods used in applied econometrics. It begins with OLS — matrix derivation, the conditional expectation function, and the best linear predictor — then moves to maximum likelihood, quasi-MLE and M-estimation, and generalized least squares. Endogeneity, instrumental variables, and GMM follow. The second half treats models for specific data structures: censored and truncated data, discrete choice, count data, panel data, dynamic panels, survival analysis, and missing data. Two closing chapters develop the modern interpretation of OLS under heterogeneous treatment effects: effect weights and outcome weights.

Examples are in R or Stata, whichever has the cleaner implementation for the procedure at hand. For applied causal-inference material — difference-in-differences, matching, doubly robust methods, mediation, and causal discovery — see the companion books Introduction to Causal Econometrics and Topics on Econometrics and Causal Inference.

How to cite

Ao, Xiang. Econometrics Guide. https://xiangao.github.io/econometrics-guide/.

@book{ao_econometrics_guide,
  author = {Ao, Xiang},
  title  = {Econometrics Guide},
  url    = {https://xiangao.github.io/econometrics-guide/}
}