Spring 2023 - ECON 838 G100
Econometrics II (4)
Class Number: 3236
Delivery Method: In Person
Develops the core tools of theoretical and applied econometrics including time series, cross sectional, and panel data methods. Topics may include limited dependent variable models, GMM, instrumental variables, ARMA models, unit roots and cointegration, fixed and random effects, incidental parameters, testing, program evaluation, nonlinear regression, semi- and nonparametric methods.
Develops the core tools of theoretical and applied econometrics including limited dependent variable models, program evaluation, nonparametric and semiparametric estimation, panel data methods and big data/machine learning methods.
Topics: Covered topics will include:
1. Limited dependent variable models (probit, logit, tobit and selection models)
2. Program evaluation (RCT, natural experiments, matching, instrumental variable models, regression discontinuity design, and control function methods)
3. Generalized method of moment
4. Nonparametric and semiparametric estimation (kernel density estimator, conditional mean function estimator, maximum score estimator and more)
5. Panel data methods (static and dynamic panel models)
6. Big data/machine learning (LASSO, regression trees and random forest)
- Midterm exam 40%
- Final Exam 60%
Wooldridge, 2010, “Econometric Analysis of Cross Section and Panel Data, Second Edition”, Hansen, 2022, “Econometrics
Li and Racine, 2017, “Nonparametric Econometrics”, Angrist and Pischke, 2009, “Mostly Harmless Econometrics”
REQUIRED READING NOTES:
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Graduate Studies Notes:
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