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- Author:
-
Chernozhukov, Victor
- Subject:
- Mathematics and Statistics, Social Sciences
- Institution Name:
- M.I.T.
- Collection:
-
MIT OpenCourseWare
- Grade Level:
- Post-secondary
- Abstract:
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.
- Languages:
- English
- Material Type:
- Assessments, Full Course, Homework and Assignments, Lecture Notes, Syllabi
- Media Format:
- Text/HTML, Downloadable docs
- Conditions of Use:
-
Creative Commons Attribution-Noncommercial-Share Alike 3.0
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
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