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Size Characteristics Of Tests For Sample Selection Bias: A Monte Carlo Comparison And Empirical Example

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  • Kazumitsu Nawata
  • Michael McAleer

Abstract

The t-test of an individual coefficient is used widely in models of qualitative choice. However, it is well known that the t-test can yield misleading results when the sample size is small. This paper provides some experimental evidence on the finite sample properties of the t-test in models with sample selection biases, through a comparison of the t-test with the likelihood ratio and Lagrange multiplier tests, which are asymptotically equivalent to the squared t-test. The finite sample problems with the t-test are shown to be alarming, and much more serious than in models such as binary choice models. An empirical example is also presented to highlight the differences in the calculated test statistics.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 20 (2001)
Issue (Month): 1 ()
Pages: 105-112

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Handle: RePEc:taf:emetrv:v:20:y:2001:i:1:p:105-112

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Related research

Keywords: Sample selection bias; t-test; Wald test; JEL Classification: C12; C24;

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Cited by:
  1. Rosalie Viney & Marion Haas & Rochelle Belkar & Denzil G. Fiebig, 2004. "Why worry about awareness in choice problems? Econometric analysis of screening for cervical cancer," Econometric Society 2004 Australasian Meetings 109, Econometric Society.
  2. repec:ebl:ecbull:v:3:y:2007:i:54:p:1-10 is not listed on IDEAS
  3. Takashi Yamagata & Chris Orme, 2005. "On Testing Sample Selection Bias Under the Multicollinearity Problem," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 467-481.
  4. Yamagata, Takashi, 2006. "The small sample performance of the Wald test in the sample selection model under the multicollinearity problem," Economics Letters, Elsevier, vol. 93(1), pages 75-81, October.
  5. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
  6. Maria Ana Odejar & Kostas Mavromaras & Mandy Ryan, 2004. "Messy Data Modelling in Health Care Contingent Valuation Studies," Econometric Society 2004 North American Summer Meetings 406, Econometric Society.
  7. Kazumitsu Nawata, 2007. "A monte carlo analysis of the type II tobit maximum likelihood estimator when the true model is the type I tobit model," Economics Bulletin, AccessEcon, vol. 3(54), pages 1-10.
  8. Belkar, R. & Fiebig, D.G., 2008. "A Monte Carlo comparison of estimators for a bivariate probit model with selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 250-256.

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