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Exclusion Bias in Sample‐Selection Model Estimators

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  • Myoung‐Jae Lee

Abstract

Exclusion restrictions are routinely used in sample‐selection models with “selection” and “outcome” equations. A false restriction, however, can cause an “exclusion bias” for the outcome equation estimator. In this paper, the specific form of the exclusion bias is derived for various sample‐selection model estimators. Furthermore, it is shown that the outcome equation parameters for regressors with zero coefficients in the selection equation are immune to exclusion bias if only one regressor is excluded. Exclusion bias, or a lack thereof, is verified through a simulation study with the regressors taken from Mroz (1987). JEL Classification Numbers: C24, C34

Suggested Citation

  • Myoung‐Jae Lee, 2003. "Exclusion Bias in Sample‐Selection Model Estimators," The Japanese Economic Review, Japanese Economic Association, vol. 54(2), pages 229-236, June.
    • Handle: RePEc:bla:jecrev:v:54:y:2003:i:2:p:229-236
    DOI: 10.1111/1468-5876.t01-1-00256
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    1. Powell, James L., 1987. "Semiparametric Estimation Of Bivariate Latent Variable Models," SSRI Workshop Series 292689, University of Wisconsin-Madison, Social Systems Research Institute.
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    Cited by:

    1. Dogan, Osman & Taspinar, Suleyman, 2016. "Bayesian Inference in Spatial Sample Selection Models," MPRA Paper 82829, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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