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Bayesian analysis of autoregressive fractionally integrated moving‐average processes
Author(s) -
Pai Jeffrey S.,
Ravishanker Nalini
Publication year - 1998
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/1467-9892.00079
Subject(s) - autoregressive fractionally integrated moving average , markov chain monte carlo , gibbs sampling , mathematics , autoregressive model , metropolis–hastings algorithm , bayesian probability , bayesian inference , inference , monte carlo method , autoregressive integrated moving average , markov chain , algorithm , long memory , econometrics , statistics , computer science , time series , artificial intelligence , volatility (finance)
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.