Binary quantile regression with local polynomial smoothing

Songnian Chen and Hanghui Zhang

Journal of Econometrics, 2015, vol. 189, issue 1, 24-40

Abstract: Manski (1975, 1985) proposed the maximum score estimator for the binary choice model under a weak conditional median restriction that converges at the rate of n−1/3 and the standardized version has a nonstandard distribution. By imposing additional smoothness conditions, Horowitz (1992) proposed a smoothed maximum score estimator that often has large finite sample biases and is quite sensitive to the choice of smoothing parameter. In this paper we propose a novel framework that leads to a local polynomial smoothing based estimator. Our estimator possesses finite sample and asymptotic properties typically associated with the local polynomial regression. In addition, our local polynomial regression-based estimator can be extended to the panel data setting. Simulation results suggest that our estimators may offer significant improvement over the smoothed maximum score estimators.

Keywords: Binary quantile regression; Smoothed maximum score estimator; Local polynomial smoothing (search for similar items in EconPapers)
JEL-codes: C14 C25 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:189:y:2015:i:1:p:24-40

DOI: 10.1016/j.jeconom.2015.06.019

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