A Geometric Approach to Weakly Identified Econometric Models
Name
AnnaMikusheva12-15.pdf
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889.65 KB
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Author(s)
Andrews, Isaiah
Mikusheva, Anna
Date Issued
May 29, 2012
Publisher
Cambridge, MA: Department of Economics, Massachusetts Institute of Technology
Series/Report no.
Working paper, Massachusetts Institute of Technology, Dept. of Economics;12-15
Abstract
Many nonlinear Econometric models show evidence of weak identification, including many Dynamic Stochastic General Equilibrium models, New Keynesian Phillips curve models, and models with forward-looking expectations. In this paper we consider minimum distance statistics and show that in a broad class of models the problem of testing under weak identification is closely related to 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 Anderson-Rubin-type statistics, which we show can be used to detect weak identification and to construct tests robust to weak identification. We apply the new method to a small-scale DSGE model and show that it provides a significant improvement over existing methods.
Subjects
weak identification
statistical differential geometry
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