Bayesian analysis of autoregressive fractionally integrated moving‐average processes

Jeffrey S. Pai and Nalini Ravishanker

Journal of Time Series Analysis, 1998, vol. 19, issue 1, 99-112

Abstract: For the autoregressive fractionally integrated moving‐average (ARFIMA) processes which characterize both long‐memory and short‐memory behavior in time series, we formulate Bayesian inference using Markov chain Monte Carlo methods. We derive a form for the joint posterior distribution of the parameters that is computationally feasible for repetitive evaluation within a modified Gibbs sampling algorithm that we employ. We illustrate our approach through two examples.

Date: 1998
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:19:y:1998:i:1:p:99-112

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