bootstrap tests for overidentification in linear regression models

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ID: 254207
2015
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Abstract
We study the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent random variables and two nuisance parameters. The distributions of the statistics are shown to have an ill-defined limit as the parameter that determines the strength of the instruments tends to zero and as the correlation between the disturbances of the structural and reduced-form equations tends to plus or minus one. This makes it impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are not too weak. We also study their power properties.
Reference Key
davidson2015econometricsbootstrap Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Russell Davidson;James G. MacKinnon
Journal developmental cognitive neuroscience
Year 2015
DOI
10.3390/econometrics3040825
URL
http://www.mdpi.com/2225-1146/3/4/825
Keywords
weak instrumentseconomics as a science

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