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THE IMPACT OF A HAUSMAN PRETEST ON THE ASYMPTOTIC SIZE OF A HYPOTHESIS TEST
Published online by Cambridge University Press: 18 August 2009
Abstract
This paper investigates the asymptotic size properties of a two-stage test in the linear instrumental variables model when in the first stage a Hausman (1978) specification test is used as a pretest of exogeneity of a regressor. In the second stage, a simple hypothesis about a component of the structural parameter vector is tested, using a t-statistic that is based on either the ordinary least squares (OLS) or the two-stage least squares estimator (2SLS), depending on the outcome of the Hausman pretest. The asymptotic size of the two-stage test is derived in a model where weak instruments are ruled out by imposing a positive lower bound on the strength of the instruments. The asymptotic size equals 1 for empirically relevant choices of the parameter space. The size distortion is caused by a discontinuity of the asymptotic distribution of the test statistic in the correlation parameter between the structural and reduced form error terms. The Hausman pretest does not have sufficient power against correlations that are local to zero while the OLS-based t-statistic takes on large values for such nonzero correlations. Instead of using the two-stage procedure, the recommendation then is to use a t-statistic based on the 2SLS estimator or, if weak instruments are a concern, the conditional likelihood ratio test by Moreira (2003).
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- Copyright © Cambridge University Press 2009
Footnotes
I would like to thank the Sloan Foundation for a 2009 fellowship and the NSF for support under grant number SES-0748922, co-editor Richard Smith, seminar participants, Don Andrews, Badi Baltagi, Ivan Canay, Gary Chamberlain, Victor Chernozhukov, Phoebus Dhrymes, Ivan Fernandez-Val, Raffaella Giacomini, William Greene, Jinyong Hahn, Bruce Hansen, Jerry Hausman, Guido Imbens, Dale Jorgenson, Anna Mikusheva, Marcelo Moreira, Ulrich Müller, Whitney Newey, Pierre Perron, Jack Porter, Zhongjun Qu, John Rust, Jim Stock, and Michael Wolf for comments, and the Economics Department at Harvard and the Cowles Foundation at Yale for their hospitality.
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