A data-driven bandwidth selection method for the smoothed maximum score estimator

Xirong Chen, Wenzheng Gao and Zheng Li

Economics Letters, 2018, vol. 170, issue C, 24-26

Abstract: Binary response regression models are useful in many economic and statistical applications. Horowitz (1992) proposes a semi-parametric estimation method, which is a smoothed version of, and has a faster convergence rate than, Manski’s maximum score estimator. The method for selecting the smoothing parameter (bandwidth) here is analogous to the plug-in method in kernel density estimation. It requires initial “pilot” values of the bandwidth to obtain the optimal bandwidth. However, this method has the disadvantage of not being fully data-driven. In this paper, we propose a data-driven bandwidth selection method by minimizing a cross-validated criterion function. Our simulation results show that the proposed method performs better than some existing methods.

Keywords: Binary response models; Smoothed maximum score estimation; Bandwidth selection (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:170:y:2018:i:c:p:24-26

DOI: 10.1016/j.econlet.2018.05.024

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