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

Zhu Yanli, Chen Haiqiang () and Lin Ming
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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

Studies in Nonlinear Dynamics & Econometrics, 2019, vol. 23, issue 5, 17

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.

Keywords: bayesian inference; Markov chain Monte Carlo; model selection; threshold model; time-varying threshold value (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/snde-2017-0114

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