14.381 Statistical Method in Economics, Fall 2006
Author(s)
Chernozhukov, Victor
Alternative Title
Statistical Method in Economics
Date Issued
December 2006
Abstract
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.
Subjects
statistical theory
econometrics
regression analysis
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
autocorrelation
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
This site (c) Massachusetts Institute of Technology 2023. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license") unless otherwise noted. The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions.
Persistent DSpace Link
