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Monte Carlo Comparison for Nonparametric Threshold Estimators

Department of Economics and Finance, University of Guelph, Guelph, ON N1G 2W1, Canada
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J. Risk Financial Manag. 2018, 11(3), 49; https://doi.org/10.3390/jrfm11030049
Received: 17 July 2018 / Revised: 13 August 2018 / Accepted: 15 August 2018 / Published: 17 August 2018
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
This paper compares the finite sample performance of three non-parametric threshold estimators via the Monte Carlo method. Our results indicate that the finite sample performance of the three estimators is not robust to the position of the threshold level along the distribution of the threshold variable, especially when a structural change occurs at the tail part of the distribution. View Full-Text
Keywords: difference kernel estimator; integrated difference kernel estimator; M-estimation; Monte Carlo; nonparametric threshold regression difference kernel estimator; integrated difference kernel estimator; M-estimation; Monte Carlo; nonparametric threshold regression
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Chen, C.; Sun, Y. Monte Carlo Comparison for Nonparametric Threshold Estimators. J. Risk Financial Manag. 2018, 11, 49.

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