Valid Inference in Partially Unstable GMM Models

37 Pages Posted: 1 Apr 2007 Last revised: 30 Dec 2009

See all articles by Hong Li

Hong Li

Brandeis University

Ulrich K. Müller

Princeton University - Department of Economics

Abstract

The paper considers time series GMM models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable parameters is asymptotically unaffected by the partial instability. In the empirical analysis of presumably stable parameters - such as structural parameters in Euler conditions - one can thus ignore moderate instabilities in other parts of the model and still obtain approximately correct inference.

Keywords: Structural Breaks, Parameter Stability Test, Contiguity, Euler Condition

JEL Classification: C32

Suggested Citation

Li, Hong and Müller, Ulrich K., Valid Inference in Partially Unstable GMM Models. Available at SSRN: https://ssrn.com/abstract=974910 or http://dx.doi.org/10.2139/ssrn.974910

Hong Li (Contact Author)

Brandeis University ( email )

Waltham, MA 02454
United States

Ulrich K. Müller

Princeton University - Department of Economics ( email )

Princeton, NJ 08544-1021
United States
609-258-3216 (Phone)
609-258-4026 (Fax)

HOME PAGE: http://www.princeton.edu/~umueller

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