Subset selection of double-threshold moving average models through the application of the Bayesian method

Jinshan Liu, Jiazhu Pan, Qiang Xia, Ying Xiao

Research output: Contribution to journalArticlepeer-review

Abstract

The Bayesian method is firstly applied for the selection of the best subset for the double-threshold moving average (DTMA) model. The Markov chain Monte Carlo (MCMC) techniques and the stochastic search variable selection (SSVS) method are used to identify the best subset model from a very large number of possible models. Simulation experiments show that the proposed method is feasible and efficient, despite the complexity being increased by the large number of subsets, and the uncertainty of the threshold and delay variables. Our method is illustrated by real data analysis on the Yen-Dollar exchange rate.
Original languageEnglish
Number of pages11
JournalStatistics and Its Interface
Publication statusAccepted/In press - 12 Apr 2021

Keywords

  • Bayesian estimation
  • Monte Carlo Markov chains
  • moving average models
  • DTMA model

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