Bayesian modelling of ARFIMA processes by Markov chain Monte Carlo methods

Bayesian modelling of ARFIMA processes by Markov chain Monte Carlo methods

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Article ID: iaor1997788
Country: United Kingdom
Volume: 15
Issue: 2
Start Page Number: 63
End Page Number: 82
Publication Date: Mar 1996
Journal: International Journal of Forecasting
Authors: ,
Keywords: markov processes
Abstract:

This article describes inference for autoregressive fractionally integrated moving average (ARFIMA) models using Markov chain Monte Carlo methods. The posterior distribution of the model parameters, corresponding to the exact likelihood function is obtained through the partial linear regression coefficients of the ARFIMA process. A Metropolis-Rao-Blackwellizallization approach is used for implementing sampling-based Bayesian inference. Bayesian model selection is discussed and implemented.

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