Geometric methods in econometrics and statistics
Name
1142100716-MIT.pdf
Size
9.68 MB
Format
Adobe PDF
Checksum (MD5)
764bf2018e0bb086330f0cd70dfae06b
Author(s)
Mukhin, Yaroslav V.(Yaroslav Vadimovich)
Advisor(s)
Whitney K. Newey, Anna Mikusheva and Victor V. Chernozhukov.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Econometrics and statistics rely on asymptotic approximations to construct hypothesis tests and confidence regions. Asymptotic approximations can also be used more abstractly to study the quality (efficiency) of estimators and tests. These approximations are closely related to local (differential) properties of the functionals of the statistical model whose values are being estimated and tested. I consider statistical models and estimands motivated by economic theory and applications and study their local and also global properties: I study the local properties of functionals to characterize the efficiency bounds of their estimators and the directions of most rapid (gradient) change with respect to different metrics of distance on the model. I use gradient flows to describe global evolutions on the statistical model governed by changes in a scalar functional. These flows can be used to describe economic policy and to study structural estimators motivated by economic theory.
Description
Thesis: Ph. D. in Economics and Statistics, Massachusetts Institute of Technology, Department of Economics, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 143-150).
Subjects
Economics.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
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