Testing Parameters in GMM Without Assuming that They Are Identified
We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic χ-super-2 distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic χ-super-2 distributions of the traditional statistics are valid. The behavior of the K statistic can be spurious around inflection points and maxima of the objective function. This inadequacy is overcome by combining the K statistic with a statistic that tests the validity of the moment equations and by an extension of Moreira's (2003) conditional likelihood ratio statistic toward GMM. We conduct a power comparison to test for the risk aversion parameter in a stochastic discount factor model and construct its confidence set for observed consumption growth and asset return series. Copyright The Econometric Society 2005.
Year of publication: |
2005
|
---|---|
Authors: | Kleibergen, Frank |
Published in: |
Econometrica. - Econometric Society. - Vol. 73.2005, 4, p. 1103-1123
|
Publisher: |
Econometric Society |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Bayesian Analysis of ARMA Models using Noninformative Priors
Kleibergen, Frank, (1997)
-
Generalized Reduced Rank Tests using the Singular Value Decomposition
Kleibergen, Frank, (2003)
-
Kleibergen, Frank, (2002)
- More ...