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Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model

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  • Fukang Zhu
  • Mengya Liu
  • Shiqing Ling
  • Zongwu Cai

Abstract

This article investigates two test statistics for testing structural changes and thresholds in predictive regression models. The generalized likelihood ratio (GLR) test is proposed for the stationary predictor and the generalized F test is suggested for the persistent predictor. Under the null hypothesis of no structural change and threshold, it is shown that the GLR test statistic converges to a function of a centered Gaussian process, and the generalized F test statistic converges to a function of Brownian motions. A Bootstrap method is proposed to obtain the critical values of test statistics. Simulation studies and a real example are given to assess the performances of the proposed tests.

Suggested Citation

  • Fukang Zhu & Mengya Liu & Shiqing Ling & Zongwu Cai, 2022. "Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 228-240, December.
    • Handle: RePEc:taf:jnlbes:v:41:y:2022:i:1:p:228-240
    DOI: 10.1080/07350015.2021.2008406
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    Cited by:

    1. Christis Katsouris, 2023. "Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models," Papers 2308.13915, arXiv.org.

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