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A Comparison Study of Bayesian Methods for a Threshold Autoregressive Model with Regime-Switching

국면전환 임계 자기회귀 분석을 위한 베이지안 방법 비교연구

Roh, Taeyoung;Jo, Seongil;Lee, Ryounghwa
노태영;조성일;이령화

  • Received : 2014.10.02
  • Accepted : 2014.11.11
  • Published : 2014.12.31

Abstract

Autoregressive models are used to analyze an univariate time series data; however, these methods can be inappropriate when a structural break appears in a time series since they assume that a trend is consistent. Threshold autoregressive models (popular regime-switching models) have been proposed to address this problem. Recently, the models have been extended to two regime-switching models with delay parameter. We discuss two regime-switching threshold autoregressive models from a Bayesian point of view. For a Bayesian analysis, we consider a parametric threshold autoregressive model and a nonparametric threshold autoregressive model using Dirichlet process prior. The posterior distributions are derived and the posterior inferences is performed via Markov chain Monte Carlo method and based on two Bayesian threshold autoregressive models. We present a simulation study to compare the performance of the models. We also apply models to gross domestic product data of U.S.A and South Korea.

Keywords

GDP;threshold autoregressive model;parametric Bayesian method;nonparametric Bayesian method;Dirichlet process prior

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Acknowledgement

Supported by : 한국연구재단