Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Bayesian Approach for Determining the Order p in Autoregressive Models

  • Kim, Chansoo (Research Institute of Computer Information and Communication, Pusan National University, Pusan, 609-735) ;
  • Chung, Younshik (Professor, Department of Statistics, Pusan National University,)
  • Published : 2001.12.01
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Abstract

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.

Keywords

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