You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Testing Parameters in GMM without Assuming That They Are Identified
Contributor:
Kleibergen, Frank
Published:
Econometric Society, 2005
Published in:
Econometrica, 73 (2005) 4, Seite 1103-1123
Language:
English
ISSN:
0012-9682;
1468-0262
Origination:
Footnote:
Description:
<p>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<sup>2</sup>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<sup>2</sup>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.</p>