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Bayesian analysis of autoregressive fractionally integrated moving‐average processes

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  • Jeffrey S. Pai
  • Nalini Ravishanker

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.

Suggested Citation

  • Jeffrey S. Pai & Nalini Ravishanker, 1998. "Bayesian analysis of autoregressive fractionally integrated moving‐average processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 99-112, January.
    • Handle: RePEc:bla:jtsera:v:19:y:1998:i:1:p:99-112
    DOI: 10.1111/1467-9892.00079
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    Cited by:

    1. Ross Doppelt & Keith O'Hara, 2018. "Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks," 2018 Meeting Papers 1212, Society for Economic Dynamics.
    2. Ossama Mikhail & Curtis J. Eberwein & Jagdish Handa, 2003. "Testing and Estimating Persistence in Canadian Unemployment," Econometrics 0311004, University Library of Munich, Germany.
    3. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.

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