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Binary quantile regression with local polynomial smoothing
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DOI：10.1016/j.jeconom.2015.06.019.png)
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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. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Binary quantile regression
Smoothed maximum score estimator
Local polynomial smoothing
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Panel data discrete choice models with lagged dependent variables具有滞后因变量的面板数据离散选择模型.png)
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