A Geometric Approach to Weakly Identified Econometric Models

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.