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Semiparametric Estimation of Heteroscedastic Binary Sample Selection Model

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  • Song Nian Chen

    (Hong Kong University of Science and Technology)

Abstract

Binary choice sample selection models are widely used in applied economics with large cross-sectional data where heteroscedaticity is typically a serious concern. Existing parametric and semiparametric estimators for the binary selection equation and the outcome equation in such models suffer from serious drawbacks in the presence of heteroscedasticity of unknown form in the latent errors. In this paper we propose some new estimators to overcome these drawbacks under a symmetry condition, robust to both nonnormality and general heterscedasticity. The estimators are shown to be $\sqrt{n}$-consistent and asymptotically normal. We also indicate that our approaches may be extended to other important models.

Suggested Citation

  • Song Nian Chen, 2000. "Semiparametric Estimation of Heteroscedastic Binary Sample Selection Model," Econometric Society World Congress 2000 Contributed Papers 0263, Econometric Society.
    • Handle: RePEc:ecm:wc2000:0263
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    References listed on IDEAS

    as
    1. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
    2. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
    3. Chen, Songnian & Lee, Lung-Fei, 1998. "Efficient Semiparametric Scoring Estimation Of Sample Selection Models," Econometric Theory, Cambridge University Press, vol. 14(4), pages 423-462, August.
    4. Chamberlain, Gary, 1992. "Efficiency Bounds for Semiparametric Regression," Econometrica, Econometric Society, vol. 60(3), pages 567-596, May.
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