14.381 Statistical Method in Economics

Fall 2006

A linear spline approximation to a curve.
Approximations of a curve by linear splines with K=3 and K=8. The curve is approximated to a reasonable extent. (Image by Prof. Victor Chernozhukov.)

Course Description

The course introduces statistical theory to prepare students for the remainder of the econometrics sequence. The emphasis of the course is to understand the basic principles of statistical theory. A brief review of probability will be given; however, this material is assumed knowledge. The course also covers basic regression analysis. Topics covered include probability, random samples, asymptotic methods, point estimation, evaluation of estimators, Cramer-Rao theorem, hypothesis tests, Neyman Pearson lemma, Likelihood Ratio test, interval estimation, best linear predictor, best linear approximation, conditional expectation function, building functional forms, regression algebra, Gauss-Markov optimality, finite-sample inference, consistency, asymptotic normality, heteroscedasticity, and autocorrelation.

Recommended Citation

For any use or distribution of these materials, please cite as follows:

Victor Chernozhukov, course materials for 14.381 Statistical Method in Economics, Fall 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].

Technical Requirements

Special software is required to use some of the files in this course: .dta.

Donate Now

Staff

Instructor:
Prof. Victor Chernozhukov

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Recitations:
One session / week
1 hour / session

Level

Graduate