On testing sample selection bias under the multicollinearity problem

T. Yamagata, C.D. Orme

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)467-481
Number of pages14
JournalEconometric Reviews
Issue number4
Publication statusPublished - Oct 2005

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