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Monte Carlo Comparison for Nonparametric Threshold Estimators
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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.
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.
Keywords
difference kernel estimator; integrated difference kernel estimator; M-estimation; Monte Carlo; nonparametric threshold regression
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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