Bayesian Subset Selection for Two-Threshold Variable Autoregressive Models

Ni Shuxia, Xia Qiang () and Liu Jinshan
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Ni Shuxia: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Xia Qiang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Liu Jinshan: School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, China

Studies in Nonlinear Dynamics & Econometrics, 2018, vol. 22, issue 4, 16

Abstract: In this paper, we propose and study an effective Bayesian subset selection method for two-threshold variable autoregressive (TTV-AR) models. The usual complexity of model selection is increased by capturing the uncertainty of the two unknown threshold levels and the two unknown delay lags. By using Markov chain Monte Carlo (MCMC) techniques with driven by a stochastic search, we can identify the best subset model from a large number of possible choices. Simulation experiments show that the proposed method works very well. As applied to the application to the Hang Seng index, we successfully distinguish the best subset TTV-AR model.

Keywords: autoregressive models; Bayesian inference; Markov chain Monte Carlo; stochastic search; two-threshold variable (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/snde-2017-0062

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