Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Recitations: 1 session / week, 1.5 hours / session

Description

This course presents micro-econometric models, including large sample theory for estimation and hypothesis testing, generalized method of moments (GMM), estimation of censored and truncated specifications, quantile regression, structural estimation, nonparametric and semiparametric estimation, treatment effects, panel data, bootstrapping, simulation methods, and Bayesian methods. The methods are illustrated with economic applications.

The first half of the course (Part A) is taught by Prof. Chernozhukov, while the second half (Part B) is taught by Prof. Newey.

Prerequisites

The prerequisites include 14.382 Econometrics or permission of the instructor.

Texts

Please see the readings.

Grading

The grades for each part count equally towards the final grade.

Part A – Chernozhukov

ACTIVITIES PERCENTAGES
Problem sets 60%
Midterm exam 40%

Part B – Newey

ACTIVITIES PERCENTAGES
Problem sets 40%
Final exam 60%

Calendar

The following three articles are referenced in the Recitation topics. Articles that pertain to lecture topics can be found in the readings.

Ahn, H., and J. L. Powell. “Semiparametric Estimation of Censored Selection Models with a Nonparametric Selection Mechanism.” Journal of Econometrics 58 (1993): 3-29.

Autor, D., L. F. Katz, and M. S. Kearney. “Rising Wage Inequality: The Role of Composition and Prices.” National Bureau of Economic Research (NBER) Working Paper No. 11628 (August 2005): 1-65.

Chernozhukov, V., and H. Hong. “Three-step Censored Quantile Regression and Extramarital Affairs.” Journal of the American Statistical Association 97, no. 459 (September 2002): 872-882.

The calendar below provides information on the course’s lecture (L) and Recitation (R) sessions. Part A consists of sessions L1-L12, while Part B consists of sessions L13-L25.

SES # TOPICS KEY DATES
L1 Methods for nonlinear models: maximum likelihood estimation (MLE), generalized method of moments (GMM), minimum distance, extremum Problem set A-1 out two days after Ses #L1
L2-L3 Large sample theory, asymptotic theory, discrete choice, censoring, and sample selection  
R1 Extremum estimators, variance estimation, hypothesis tests  
L4-L5 Large sample theory, asymptotic theory, discrete choice, censoring, and sample selection (cont.)  
R2 ML computation: probit using ordinary least squares (OLS) command, hypothesis tests; two-step estimation: Heckman correction

Problem set A-1 due

Problem set A-2 out

L6 Bootstrap, subsampling, and finite-sample methods  
R3 Bootstrap  
L7 Bootstrap, subsampling, and finite-sample methods (cont.)  
L8 Quantile regression (QR) and distributional methods  
R4 Quantile regression: integral transformation/Skorohod representation, conditional means vs. conditional quantiles, inference for quantile regression, and high-tech application: wage decompositions (Autor, Katz, and Kearney 2005) Problem set A-2 due
L9 Quantile regression (QR) and distributional methods (cont.)  
R5 QR applications: 3-step procedure for censored QR (Chernozhukov and Hong 2002); digression: duration models; brief introduction to R, wage decomposition  
L10-L11 Bayesian and quasi-Bayesian methods (from a classical view)  
R6 Accept-reject sampling, the Gibbs sampler, and Monte Carlo optimization  
L12 Bounds and partial identification

Problem set A-3 out

Midterm exam two days after Ses #L12

L13-L14 GMM: identification, estimation, testing, bias, selecting moments Problem set B-1 out on Ses #L14
R7 Duration models: main concepts, practical issues; GMM: higher-order bias for two stage least squares (2SLS) estimation, adding moments and efficiency  
L15 Weak and many instruments  
L16 Nonparametric estimation Problem set A-3 due
R8 Nonparametric regression: theoretical bias and variance of the Nadaraya-Watson estimator, confidence intervals, bandwidth choice: cross-validation in kernel regression  
L17 Nonparametric estimation (cont.)

Problem set B-1 due

Problem set B-2 out one day after Ses #L17

R9 GMM with condition moment restriction: optimal IV vs. efficient weighting matrix, example from Problem set B-1; nonparametric regression: kernel regression asymptotics, local linear estimation, bandwidth selection, generalized cross-validation  
L18-L19 Semiparametric estimation  
L20 Treatment effects  
L21 Nonlinear models in panel data Problem set B-2 due
R10 Series estimation and discontinuities, an example for the partially linear model: semiparametric selection models (Ahn and Powell 1993); treatment effects: the LaLonde debate Problem set B-3 out
L22 Nonlinear models in panel data (cont.)  
L23 Economic modeling and econometrics  
R11 Nonlinear panel data: incidental parameters problem, conditional MLE: Logit case; method of simulated moments (MSM): brief introduction to numerical integration, simulated estimation  
L24-L25 Economic modeling and econometrics (cont.)

Problem set B-3 due

Final exam seven days after Ses #L25

Course Info

Learning Resource Types

grading Exams
notes Lecture Notes
assignment Problem Sets