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Time-simultaneous prediction band for a time series

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  • Dag Kolsrud

    (Statistics Norway, Oslo, Norway)

Abstract

I propose principles and methods for the construction of a time-simultaneous prediction band for a univariate time series. The methods are entirely based on a learning sample of time trajectories, and make no parametric assumption about its distribution. Hence, the methods are general and widely applicable. The expected coverage probability of a band can be estimated by a bootstrap procedure. The estimate is likely to be less than the nominal level. Expected lack of coverage can be compensated for by increasing the coverage in the learning sample. Applications to simulated and empirical data illustrate the methods. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Dag Kolsrud, 2007. "Time-simultaneous prediction band for a time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 171-188.
  • Handle: RePEc:jof:jforec:v:26:y:2007:i:3:p:171-188 DOI: 10.1002/for.1020
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    References listed on IDEAS

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    1. Kim, Jae H., 2004. "Bootstrap prediction intervals for autoregression using asymptotically mean-unbiased estimators," International Journal of Forecasting, Elsevier, vol. 20(1), pages 85-97.
    2. Bo Yang & John E. Kolassa, 2004. "Smooth and Accurate Multivariate Confidence Regions," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1072-1081, December.
    3. Clements, Michael P. & Taylor, Nick, 2001. "Bootstrapping prediction intervals for autoregressive models," International Journal of Forecasting, Elsevier, vol. 17(2), pages 247-267.
    4. Peter Hall, 2004. "Nonparametric confidence intervals for receiver operating characteristic curves," Biometrika, Biometrika Trust, vol. 91(3), pages 743-750, September.
    5. Touhami Abdelkhalek & Jean-Marie Dufour, 1998. "Statistical Inference For Computable General Equilibrium Models, With Application To A Model Of The Moroccan Economy," The Review of Economics and Statistics, MIT Press, pages 520-534.
    6. Reeves, Jonathan J., 2005. "Bootstrap prediction intervals for ARCH models," International Journal of Forecasting, Elsevier, vol. 21(2), pages 237-248.
    7. Thomas H. Scheike & Mei-Jie Zhang, 2003. "Extensions and Applications of the Cox-Aalen Survival Model," Biometrics, The International Biometric Society, vol. 59(4), pages 1036-1045, December.
    8. Nico Keilman & Dinh Quang Pham & Arve Hetland, 2002. "Why population forecasts should be probabilistic - illustrated by the case of Norway," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 6(15), pages 409-454, May.
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    Cited by:

    1. Li, J.S.H. & Ng, A.C.Y. & Chan, W.S., 2013. "Stochastic life table forecasting: A time-simultaneous fan chart application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 98-107.
    2. Dag Kolsrud, 2015. "A Time‐Simultaneous Prediction Box for a Multivariate Time Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(8), pages 675-693, December.
    3. Li, Johnny Siu-Hang & Chan, Wai-Sum, 2011. "Time-simultaneous prediction bands: A new look at the uncertainty involved in forecasting mortality," Insurance: Mathematics and Economics, Elsevier, vol. 49(1), pages 81-88, July.

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