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Improved Bootstrap Prediction Intervals For Autoregressions

Author

Listed:
  • F. Jay Breidt
  • Richard A. Davis
  • William T. M. Dunsmuir

Abstract

. We consider bootstrap construction and calibration of prediction intervals for nonGaussian autoregressions. In particular, we address the question of prediction conditioned on the last p observations of the process, for which we offer an exact simulation technique and an approximate bootstrap approach. In simulations for a variety of first‐order autoregressions, we compare various nonparametric prediction intervals and find that calibration gives reasonably narrow prediction intervals with the lowest coverage probability mean squared error among the methods used.

Suggested Citation

  • F. Jay Breidt & Richard A. Davis & William T. M. Dunsmuir, 1995. "Improved Bootstrap Prediction Intervals For Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(2), pages 177-200, March.
    • Handle: RePEc:bla:jtsera:v:16:y:1995:i:2:p:177-200
    DOI: 10.1111/j.1467-9892.1995.tb00229.x
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    References listed on IDEAS

    as
    1. Paul Kabaila, 1993. "On Bootstrap Predictive Inference For Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(5), pages 473-484, September.
    2. Jens‐Peter Kreiss & Jürgen Franke, 1992. "Bootstrapping Stationary Autoregressive Moving‐Average Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(4), pages 297-317, July.
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