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.)
Instructor(s)
Prof. Victor Chernozhukov
MIT Course Number
14.381
As Taught In
Fall 2006
Level
Graduate
Translated Versions
Course Description
Course Features
Course Description
This course is divided into two sections, Part I and Part II. Part I provides an introduction to statistical theory and can be found by visiting 14.381 Fall 2018.
Part II, found here, prepares 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.
Other Versions
Other OCW Versions
This subject is divided into two sections. The Fall 2018 version covers topics taught in the first half of the course, and the Fall 2006 version covers the second half of the course.