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Bayesian Analysis of Long Memory and Persistence using ARFIMA Models

Author

Listed:
  • Gary Koop

    (Dept of Economics, University of Leicester, UK)

  • Eduardo Ley

    (IMF, Washington DC, USA)

  • Jacek Osiewalski

    (Academy of Economics, Krakow, Poland)

  • Mark F.J. Steel

    (Dept of Statistics, University of Warwick, UK)

Abstract

This paper provides a Bayesian analysis of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. We discuss in detail inference on impulse responses, and show how Bayesian methods can be used to (i) test ARFIMA models against ARIMA alternatives, and (ii) take model uncertainty into account when making inferences on quantities of interest. Our methods are then used to investigate the persistence properties of real U.S. GNP.

Suggested Citation

  • Gary Koop & Eduardo Ley & Jacek Osiewalski & Mark F.J. Steel, 1995. "Bayesian Analysis of Long Memory and Persistence using ARFIMA Models," Econometrics 9505001, University Library of Munich, Germany, revised 22 Jun 2004.
    • Handle: RePEc:wpa:wuwpem:9505001
    Note: PDF replaced to display the graphics correctly. Published in The Journal of Econometrics, 76:1-2 (January), pages 149-170, 1997.
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Fractionally Integrated Models; Impulse Responses; Time Series; Trend Stationarity; Unit Root;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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