How to Learn Econometrics

Review matrix operations, vector spaces, partial derivatives, and optimization, as econometrics relies heavily on matrix notation and calculus-based derivations.

Learn OLS estimation, the Gauss-Markov assumptions, hypothesis testing on coefficients, R-squared, and the mechanics of multiple regression with several regressors.

Study heteroscedasticity, multicollinearity, omitted variable bias, functional form misspecification, and serial correlation. Learn diagnostic tests and remedies for each problem.

Understand endogeneity, IV estimation via 2SLS, weak instruments, and quasi-experimental methods including difference-in-differences and regression discontinuity designs.

Cover AR, MA, and ARMA models, stationarity and unit root tests, cointegration, error correction models, vector autoregression, and GARCH volatility models.

Explore fixed and random effects models, the Hausman test, dynamic panels, limited dependent variable models (logit, probit, Tobit), and sample selection correction.

Apply all methods to real datasets using Stata, R, or Python. Replicate published empirical studies, conduct original analyses, and present results following professional standards.