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M-Estimation of a Nonparametric Threshold Regression Model

Author

Listed:
  • Daniel J. Henderson

    (University of Alabama)

  • Christopher F. Parmeter

    (University of Miami)

  • Liangjun Su

    (Singapore Management University)

Abstract

The present work uses semiparametric M-estimation to construct an estimator for a threshold parameter in a nonparametric regression model. Given that this parameter is only weakly identified, we develop a set of sufficient conditions whereby our semiparametric M-estimator is consistent and asymptotically normal. Our work extends the theory of Chen, Linton and Van Keilegom (2003) to settings where there is weak identification for a semiparametric model. A range of Monte Carlo simulations and three empirical examples (threshold, asymmetric time series and regression discontinuity) support the asymptotic developments.

Suggested Citation

  • Daniel J. Henderson & Christopher F. Parmeter & Liangjun Su, 2017. "M-Estimation of a Nonparametric Threshold Regression Model," Working Papers 2017-15, University of Miami, Department of Economics.
    • Handle: RePEc:mia:wpaper:2017-15
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    File URL: https://www.herbert.miami.edu/_assets/files/repec/WP2017-15.pdf
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    References listed on IDEAS

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    Cited by:

    1. Lee, Yoonseok & Wang, Yulong, 2023. "Threshold regression with nonparametric sample splitting," Journal of Econometrics, Elsevier, vol. 235(2), pages 816-842.

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    More about this item

    Keywords

    Change Point; M-Estimation; Nonparametric Threshold Regression; Regression Discontinuity; Structural Change Publication Status: Submitted;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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