A graduate course in econometric theory and applied methods. The sequence develops probability and asymptotic foundations, linear estimation and inference, identification, cross-sectional and panel methods, time-series models, cointegration, and causal research design.
What you will learn
Students completing the course should be able to:
- derive OLS and IV estimators and characterize their statistical properties;
- distinguish identification from estimation and inference;
- select covariance estimators consistent with the data dependence structure;
- formulate and estimate panel and time-series models;
- evaluate cointegration, dynamic systems, and causal designs;
- conduct Monte Carlo experiments and produce reproducible empirical research.
How the course is organized
Each lecture session has a full set of written notes: learning objectives, formal development with derivations, worked examples, a problem set, a computational lab and readings. Problem sets with solution guidance and examination specifications are under Assignments and Exams.
Part I · Linear regression and inference Lectures 1–4
Part II · Endogeneity and identification Lectures 5–6
Part III · Panel data Lectures 7–8
Part IV · Time-series econometrics Lectures 9–12
Part V · Causal inference and research practice Lectures 13–14
Assessment
Problem sets 30%; replication project 25%; midterm 20%; final applied paper 25%. Graduate extension requires asymptotic derivations, identification defense, and Monte Carlo comparison.
Core references
- Hayashi, Econometrics.
- Wooldridge, Econometric Analysis of Cross Section and Panel Data.
- Greene, Econometric Analysis.
- Hamilton, Time Series Analysis.
- Lütkepohl, New Introduction to Multiple Time Series Analysis.
- Angrist and Pischke, Mostly Harmless Econometrics.