Econometrics methods course slides by Professor Òscar Jordà

Outline of Topics:
• Topic 0. Review of Basic Concepts. Quick review of bivariate regression,
estimation, inference and evaluation. Statistical foundations. Basic statistical
concepts you should know from 239. Basic matrix algebra you should know.

• Topic 1. Multivariate Regression: Part I. Statement of the objective of regression
analysis. Basic assumptions. Three approaches: method of moments (MM);
ordinary least squares (OLS); and maximum likelihood (MLE). Basic derivations
for each of these methods.

• Topic 2. Multivariate Regression Part II. Properties of MM/OLS/MLE
estimators – finite and large sample properties. Basic theory for extremum
estimators.

• Topic 3. Inference I. Elements of a test. Wald, likelihood ratio (LR) and
Lagrange multiplier (LM) tests; single and multiple hypothesis testing; asymptotic
distribution of common tests.
• Topic 4. Inference II. Confidence regions and simultaneous testing procedures.
Simulation-based testing. Assessing the size and power of a test with Monte Carlo
techniques. The bootstrap.

• Topic 5. Extensions to the basic framework I. Heteroskedasticity and
autocorrelation – testing and generalized least-squares.

• Topic 6. Instrumental Variable Regression. Causation versus correlation. Basic
ideas. Two stage least squares. Generalized Method of Moments.

• Topic 7. Extensions to the basic framework II. Nonlinear regression. Limited
dependent variable regression and applications of MLE.

• Topic 8. Introduction to Time Series Data. Introduction to basic concepts.
Stationarity. ARMA models.