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Threshold Estimation via Group Orthogonal Greedy Algorithm

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  • Ngai Hang Chan
  • Ching-Kang Ing
  • Yuanbo Li
  • Chun Yip Yau

Abstract

A threshold autoregressive (TAR) model is an important class of nonlinear time series models that possess many desirable features such as asymmetric limit cycles and amplitude-dependent frequencies. Statistical inference for the TAR model encounters a major difficulty in the estimation of thresholds, however. This article develops an efficient procedure to estimate the thresholds. The procedure first transforms multiple-threshold detection to a regression variable selection problem, and then employs a group orthogonal greedy algorithm to obtain the threshold estimates. Desirable theoretical results are derived to lend support to the proposed methodology. Simulation experiments are conducted to illustrate the empirical performances of the method. Applications to U.S. GNP data are investigated.

Suggested Citation

  • Ngai Hang Chan & Ching-Kang Ing & Yuanbo Li & Chun Yip Yau, 2017. "Threshold Estimation via Group Orthogonal Greedy Algorithm," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 334-345, April.
    • Handle: RePEc:taf:jnlbes:v:35:y:2017:i:2:p:334-345
    DOI: 10.1080/07350015.2015.1064820
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    Cited by:

    1. Chih‐Hao Chang & Kam‐Fai Wong & Wei‐Yee Lim, 2023. "Threshold estimation for continuous three‐phase polynomial regression models with constant mean in the middle regime," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(1), pages 4-47, February.

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