Time: Spring 2021; Mondays 9am; Wednesdays 11:15am
Location: Seminari C
Faculty: Minyoung Rho ([email protected])
Teaching Assistance: Jacek Barszczewski ([email protected])
Course Webpage: https://github.com/minyoungrho/Econometrics2
In this course, we study the estimation of structural economic models. Economic theory implies the structure of the observed variables in the data, which we can exploit to estimate the parameters of the model. We focus on extremum estimators (EE): maximum likelihood estimator (MLE), generalized method of moments (GMM), and etc. For a given estimator, we establish the large sample property (consistency and asymptotic normality) under certain conditions, and test hypotheses about the parameters of interest.
We also study other advancements in modern Econometrics such as nonparmetric estimation, which has no misspecification error but has a slower rate of convergence compared to parametric estimation, bayesian methods and quantile regressions.
In this course, we will use the Julia Programming Language to study empirical counterparts to the theories introduced.
Lectures:
| Date | Keywords* | Content | Reading |
|---|---|---|---|
| April 6 | EE | Introduction; Consistency | Hayashi, 7.1-7.2 |
| April 12 | EE | Asymptotic Normality & Hypothesis Testing | Hayashi, 7.3-7.4 |
| April 14 | MLE | Examples | Cameron and Trivedi, Ch. 5; Creel, Ch.15 |
| April 19 | GMM | Examples | Creel, Ch.16 |
| April 21 | NumOp | Search, Derivative Based Methods | Creel, Ch.12 |
| April 26 | NumOp | Nonlinear Optimization; Constrained Optimization | Creel, Ch.12 |
| April 28 | Nerlove | OLS, MLE, GMM, Restricted Optimization, and Hypothesis Testing | Creel, Ch. 6 |
| May 3 | Bayesian | Prior, Posterior, Bayes Rules, and Markov chain Monte Carlo (MCMC) | Creel, Ch. 18; Mikusheva’s MIT OpenCourseWare notes Lectures 23-25; Rossi et al. (2005) Chapters 2 & 3 |
| May 5 | Bayesian | Metropolis-Hastings algorithm and Gibbs Sampling | Creel, Ch. 18; Mikusheva’s MIT OpenCourseWare notes Lectures 23-25; Rossi et al. (2005) Chapters 2 & 3 |
| May 10 | TimeSeries | AR, MA, ARCH, and GARCH | Creel, Ch. 17 |
| May 12 | TimeSeries; Panel | Model Selection; Introduction to Panel Data | Wasserman, 13.6 |
| May 17 | Panel | Fixed-Effects, Random-Effects, and Hausman Test | Creel, Ch. 19; Arellano's Notes: Static; Dynamic I; Dynamic II |
| May 19 | Nonparametric | Introduction, Nadaraya-Watson Kernel Estimator | Wasserman, Ch.20; Creel, Ch. 20 |
| May 26 | Quantile | Quantile Regression | Creel, Ch. 21 |
| June 9 (11-13) | Materials Covered in Final | Research Overview & Review | |
| June 14 (10-13) | Final Exam |
*Keywords (or corresponding file name in lectures folder):
- extremum estimators (EE)
- maximum likelihood estimator (MLE)
- generalized method of moments (GMM)
- numerical optimization (NumOp)
- quantile regression (Quantile)
TA: Contents
Problem Sets:
| Problem Set # | Link | Assign Date | Due Date |
|---|---|---|---|
| 1 | PS1;PDF | April 12 | April 26 |
| 2 | PS2;PDF | April 26, last updated May 1 | May 10 |
| 3 | PS3;PDF | May 10, last updated May 11 | May 24 |
- 3 Problem sets (60%)
- Final exam (40%)
The references for the course materials will be updated throughout the course.
- https://quantecon.org/
- Creel. https://github.com/mcreel/Econometrics/blob/master/econometrics.pdf
- Hayashi, F. (2000). Econometrics.
- Amemiya, T. (1985). Advanced Econometrics.
- Davidson, R., MacKinnon, J. G. (2004). Econometric theory and methods.
- Cameron, A.C. and Trivedi, P.K. (2005). Microeconometrics: methods and applications.
- Wasserman, L. (2013). All of Statistics. URL: https://www.stat.cmu.edu/~larry/all-of-statistics/
- Rossi, P. E., Allenby, G. M. and McCulloch, R. (2005). Bayesian Statistics and Marketing.
- Mikusheva’s Time Series MIT OpenCourseWare notes
- Arellano's Panel Data Notes: Static; Dynamic I; Dynamic II
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