We construct a novel statistic to test hypothezes on subsets of the structural parameters in an Instrumental Variables (IV) regression model. We derive the chi squared limiting distribution of the statistic and show that it has a degrees of freedom parameter that is equal to the number of structural parameters on which the hypothesis is specified. The statistic has this limiting distribution regardless of the quality of the instruments for the endogenous variables associated with these structural parameters. The instruments have to be valid for the endogenous variables associated with the remaining structural parameters. We analyze the relationship of the novel statistic with the Lagrange Multiplier, the Likelihood Ratio and the GMM over-identification statistic from Stock and Wright (2000). Chi squared limiting distributions for the first two statistics only hold when the instruments are valid for all endogenous variables. A chi squared limiting distribution for the GMM over-identification statistic is obtained under the same conditions as for our novel statistic but has a larger degrees of freedom parameter. For some artificial datasets, we compute power curves and p-value plots that result from the different statistics. We apply the statistic to an IV regression of education on earnings from Card (1995).
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)