Authors
Date (dd-mm-yyyy)
2007
Title
Inference on subsets of parameters in GMM without assuming identification
Publication Year
2007
Number of pages
36
Publisher
AmsterdamFaculteit Economie en Bedrijfskunde
Document type
Working paper
Faculty
Faculty of Economics and Business (FEB)
Institute
Amsterdam School of Economics Research Institute (ASE-RI)
Abstract
We construct an upper bound on the limiting distributions of the identifica-
tion robust GMM statistics for testing hypotheses that are specified on subsets of
the parameters. The upper bound corresponds to the limiting distribution that
results when the unrestricted parameters are well identified. It therefore leads
to more powerful tests than those that result from using projection arguments
on tests on all the parameters. The upper bound only applies when the unre-
stricted parameters are estimated using the continuous updating estimator. The
critical values that result from the upper bound lead to conservative tests when
the unrestricted parameters are not well-identified.
Permalink
https://hdl.handle.net/11245/1.291422