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Monte Carlo Comparison for Nonparametric Threshold Estimators

Author

Listed:
  • Chaoyi Chen

    (Department of Economics and Finance, University of Guelph, Guelph, ON N1G 2W1, Canada)

  • Yiguo Sun

    (Department of Economics and Finance, University of Guelph, Guelph, ON N1G 2W1, Canada)

Abstract
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.

Suggested Citation

  • Chaoyi Chen & Yiguo Sun, 2018. "Monte Carlo Comparison for Nonparametric Threshold Estimators," JRFM, MDPI, vol. 11(3), pages 1-15, August.
    • Handle: RePEc:gam:jjrfmx:v:11:y:2018:i:3:p:49-:d:164335
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    References listed on IDEAS

    as
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    7. Kourtellos, Andros & Stengos, Thanasis & Sun, Yiguo, 2022. "Endogeneity In Semiparametric Threshold Regression," Econometric Theory, Cambridge University Press, vol. 38(3), pages 562-595, June.
    8. Yu, Ping & Phillips, Peter C.B., 2018. "Threshold regression with endogeneity," Journal of Econometrics, Elsevier, vol. 203(1), pages 50-68.
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    Cited by:

    1. Thanasis Stengos, 2019. "Nonparametric Econometric Methods and Applications," JRFM, MDPI, vol. 12(4), pages 1-3, November.

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