THE IMPACT OF A HAUSMAN PRETEST ON THE ASYMPTOTIC SIZE OF A HYPOTHESIS TEST

• Published in: Econometric theory Vol. 26; no. 2; pp. 369 - 382
• Main Author: Guggenberger, Patrik
• Format: Journal Article
• Language: English
• Published: New York, USA Cambridge University Press 01.04.2010
• Subjects: Asymptotic methods; Conditional probabilities; Correlations; Critical values; Distribution; Econometrics; Economic models; Estimation; Estimators; Hypotheses; Inference; Instrumental variables; Instrumental variables estimation; Model testing; Null hypothesis; Probabilities; Probability theory; Regression analysis; Significance tests; Statistical models; Studies; Variables
• ISSN: 0266-4666; 1469-4360
• DOI: 10.1017/S0266466609100026

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).