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Threshold models with time-varying threshold values and their application in estimating regime-sensitive Taylor rules

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  • Zhu Yanli

    (Hohai University, Business School, Institute of Industrial Economics, Nanjing, Jiangsu, China)

  • Chen Haiqiang

    (Xiamen University, The Wang Yanan Institute for Studies in Economics, MOE Key Laboratory of Econometrics, Xiamen, China)

  • Lin Ming

    (Xiamen University, Fujian Provincial Key Laboratory of Statistics, Xiamen, Fujian, China)

Abstract

The literature of time series models with threshold effects makes the assumption of a constant threshold value over different periods. However, this time-homogeneity assumption tends to be too restrictive owing to the fact that the threshold value that triggers regime switching could possibly be time-varying. This study herein proposes a threshold model in which the threshold value is assumed to be a latent variable following an autoregressive (AR) process. The newly proposed model was estimated using a Markov Chain Monte Carlo (MCMC) algorithm under a Bayesian framework. The Monte Carlo simulations are presented to assess the effectiveness of the Bayesian approaches. An illustration of the model was made through an application to a regime-sensitive Taylor rule employing U.S. data.

Suggested Citation

  • Zhu Yanli & Chen Haiqiang & Lin Ming, 2019. "Threshold models with time-varying threshold values and their application in estimating regime-sensitive Taylor rules," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(5), pages 1-17, December.
    • Handle: RePEc:bpj:sndecm:v:23:y:2019:i:5:p:17:n:4
    DOI: 10.1515/snde-2017-0114
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