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Bayesian long-run prediction in time series models

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  • Koop, Gary
  • Osiewalski, Jacek
  • Steel, Mark F. J.

Abstract

This paper considers Bayesian long-run prediction in time series models. We allow time series to exhibit stationary or non-stationary behavior and show how differences between prior structures which have little effect on posterior inferences can have a large effect in a prediction exercise. In particular, the Jeffreys' prior given in Phillips (1991) is seen to prevent the existence of one-period ahead predictive moments. A Bayesian counterpart is provided to Sampson (1991) who takes parameter uncertainty into account in a classical framework. An empirical example illustrates our results.
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Suggested Citation

  • Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1995. "Bayesian long-run prediction in time series models," Journal of Econometrics, Elsevier, vol. 69(1), pages 61-80, September.
    • Handle: RePEc:eee:econom:v:69:y:1995:i:1:p:61-80
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    9. Koop, Gary & Steel, Mark F J, 1994. "A Decision-Theoretic Analysis of the Unit-Root Hypothesis Using Mixtures of Elliptical Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(1), pages 95-107, January.
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