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Heckman sample selection estimators under heteroskedasticity

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Abstract

This paper studies the properties of two Heckman sample selection estimators, full information maximum likelihood (FIML) and limited information maximum likelihood (LIML), under heteroskedasticity. In this case, FIML is inconsistent while LIML can be consistent in certain settings. For the LIML estimator, we provide robust asymptotic variance formulas, not currently provided with standard Statacommands. Since heteroskedasticity affects these two estimators’ performance, this paper also offers guidance on how to properly test for heteroskedasticity. We propose a new demeaned Breusch–Pagan test to detect general heteroskedasticity in sample selection settings as well as a test for when LIML is consistent under heteroskedasticity. The Monte Carlo simulations illustrate that both of the proposed test procedures perform well.

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  • Alyssa Carlson & Wei Zhao, 2023. "Heckman sample selection estimators under heteroskedasticity," Working Papers 2303, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:2303
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    Keywords

    st0001; sample selection; heteroskedasticty; Bruesh–Pagan test; Hausman test;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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