A Geometric Approach to Nonlinear Econometric Models
Name
A Geometric Approach to Nonlinear Econometric Models.pdf
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433.41 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Andrews, Isaiah
Mikusheva, Anna
Date Issued
May 2016
Journal
Econometrica
Publisher
The Econometric Society
Citation
Andrews, Isaiah, and Anna Mikusheva. “A Geometric Approach to Nonlinear Econometric Models.” Econometrica 84.3 (2016): 1249–1264.
Version
Author's final manuscript
Abstract
Conventional tests for composite hypotheses in minimum distance models can be unreliable when the relationship between the structural and reduced‐form parameters is highly nonlinear. Such nonlinearity may arise for a variety of reasons, including weak identification. In this note, we begin by studying the problem of testing a “curved null” in a finite‐sample Gaussian model. Using the curvature of the model, we develop new finite‐sample bounds on the distribution of minimum‐distance statistics. These bounds allow us to construct tests for composite hypotheses which are uniformly asymptotically valid over a large class of data generating processes and structural models.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.3982/ECTA12030
