Preprint Article Version 1 This version is not peer-reviewed

Monte Carlo Comparison for Nonparametric Threshold Estimators

Version 1 : Received: 17 July 2018 / Approved: 18 July 2018 / Online: 18 July 2018 (08:24:47 CEST)

A peer-reviewed article of this Preprint also exists.

Chen, C.; Sun, Y. Monte Carlo Comparison for Nonparametric Threshold Estimators. J. Risk Financial Manag. 2018, 11, 49. Chen, C.; Sun, Y. Monte Carlo Comparison for Nonparametric Threshold Estimators. J. Risk Financial Manag. 2018, 11, 49.

Journal reference: J. Risk Financial Manag. 2018, 11, 49
DOI: 10.3390/jrfm11030049

Abstract

This paper compares the finite sample performance of three non-parametric threshold estimators via Monte Carlo method. Our results show that the finite sample performance of the three estimators is not robust to the relative position of the threshold level along the distribution of threshold variable, especially when a structural change occurs at the tail part of the distribution.

Subject Areas

difference kernel estimator; integrated difference kernel estimator; M-estimation; Monte Carlo; nonparametric threshold regression

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