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An Integrated Kernel-Weighted Smoothed Maximum Score Estimator For The Partially Linear Binary Response Model

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  • Krief, Jerome M.

Abstract

This paper considers a binary response model with a partially linear latent equation, where ϕ is an unknown function and β is a finite-dimensional parameter of interest. Using the principle of smoothed maximum score estimation (Horowitz, 1992; Econometrica 60(3), 505–531), a consistent and asymptotically normal (C.A.N.)estimator for β is proposed under the restriction that the median of the error conditional on the covariates is equal to 0. Furthermore, the rate of convergence in probability is close to the parametric rate, if certain functions admit enough derivatives. This method neither restricts the form of heteroskedasticity in the error term nor suffers from the curse of dimensionality whenever ϕ is multivariate. Some Monte Carlo experiments suggest that this estimator performs well compared with conventional estimators.

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  • Krief, Jerome M., 2014. "An Integrated Kernel-Weighted Smoothed Maximum Score Estimator For The Partially Linear Binary Response Model," Econometric Theory, Cambridge University Press, vol. 30(3), pages 647-675, June.
    • Handle: RePEc:cup:etheor:v:30:y:2014:i:03:p:647-675_00
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    Cited by:

    1. Carlson, Alyssa, 2023. "Relaxing conditional independence in an endogenous binary response model," Journal of Econometrics, Elsevier, vol. 232(2), pages 490-500.
    2. Chen, Xirong & Gao, Wenzheng & Li, Zheng, 2018. "A data-driven bandwidth selection method for the smoothed maximum score estimator," Economics Letters, Elsevier, vol. 170(C), pages 24-26.
    3. Chen, Songnian & Zhang, Hanghui, 2015. "Binary quantile regression with local polynomial smoothing," Journal of Econometrics, Elsevier, vol. 189(1), pages 24-40.

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