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Estimation and Inference for the Threshold Model with Hybrid Stochastic Local Unit Root Regressors

Author

Listed:
  • Chaoyi Chen

    (Magyar Nemzeti Bank (Central Bank of Hungary), 1013 Budapest, Hungary
    MNB Institute, John von Neumann University, 6000 Kecskemét, Hungary
    These authors contributed equally to this work.)

  • Thanasis Stengos

    (Department of Economics and Finance, University of Guelph, Guelph, ON N1G 2W1, Canada
    These authors contributed equally to this work.)

Abstract

In this paper, we study the estimation and inference of the threshold model with hybrid local stochastic unit root regressors. Our main contribution is to propose an estimator that generalizes the threshold model with various forms of nonstationary regressors and to obtain its limiting distribution theory. In particular, our proposed model generalizes the threshold model with unit root, local-to-unity, and stochastic unit root regressors. We provide the estimation strategy for the least squares estimator and derive the asymptotic results for the proposed estimator. Depending on the diminishing rate of the threshold effect, we find that the limiting distribution of the threshold estimator takes different forms. Monte Carlo simulations are used to assess our proposed estimator’s finite sample performance, which is found to perform well.

Suggested Citation

  • Chaoyi Chen & Thanasis Stengos, 2022. "Estimation and Inference for the Threshold Model with Hybrid Stochastic Local Unit Root Regressors," JRFM, MDPI, vol. 15(6), pages 1-15, May.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:6:p:242-:d:826559
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    References listed on IDEAS

    as
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