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On testing sample selection bias under the multicollinearity problem

Yamagata, T. and Orme, C.D. (2005) On testing sample selection bias under the multicollinearity problem. Econometric Reviews, 24 (4). pp. 467-481. ISSN 0747-4938

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Abstract

This paper reviews and extends the literature on the finite sample behavior of tests for sample selection bias. Monte Carlo results show that, when the “multicollinearity problem” identified by Nawata (1993) is severe, (i) the t-test based on the Heckman-Greene variance estimator can be unreliable, (ii) the Likelihood Ratio test remains powerful, and (iii) nonnormality can be interpreted as severe sample selection bias by Maximum Likelihood methods, leading to negative Wald statistics. We also confirm previous findings (Leung and Yu, 1996) that the standard regression-based t-test (Heckman, 1979) and the asymptotically efficient Lagrange Multiplier test (Melino, 1982), are robust to nonnormality but have very little power.

Item Type: Article
Institution: The University of York
Academic Units: The University of York > Economics and Related Studies (York)
Depositing User: York RAE Import
Date Deposited: 04 Jun 2009 13:45
Last Modified: 04 Jun 2009 13:45
Published Version: http://dx.doi.org/10.1080/02770900500406132
Status: Published
Publisher: Taylor & Francis
Identification Number: 10.1080/02770900500406132
URI: http://eprints.whiterose.ac.uk/id/eprint/6020

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