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Threshold effect in varying coefficient models with unknown heteroskedasticity

Author

Listed:
  • Yuanqing Zhang

    (Shanghai University of International Business and Economics)

  • Chunrong Ai

    (The Chinese University of Hong Kong)

  • Yaqin Feng

    (Ohio University)

Abstract

This paper extends the threshold regression to threshold effect in varying coefficient model. We allow for either cross-section or time series observations. Estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the regression slopes) is developed. The distribution of threshold estimates is found to be non-standard. Under some sufficient conditions, we show that the proposed estimator for regression slopes is root-n consistent and asymptotically normally distributed, and that the proposed estimator for the varying coefficient is consistent and also asymptotically normal distributed but at a rate slower than root-n. Consistent estimators for the asymptotic variances of the proposed estimators are provided. Monte Carlo simulations are presented to assess the performance of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the relationship between environmental regulation and regional technological innovation study.

Suggested Citation

  • Yuanqing Zhang & Chunrong Ai & Yaqin Feng, 2024. "Threshold effect in varying coefficient models with unknown heteroskedasticity," Computational Statistics, Springer, vol. 39(3), pages 1165-1181, May.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:3:d:10.1007_s00180-023-01335-7
    DOI: 10.1007/s00180-023-01335-7
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