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Introduction to prediction in classical time series models (in Russian)

Author

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  • Alexander Tsyplakov

    (Novosibirsk State University, Russia)

Abstract

This essay discusses basic notions of time series prediction and states traditional approaches to prediction in classical Box-Jenkins models, vector autoregressions, and autoregressive models with conditional heteroskedasticity.

Suggested Citation

  • Alexander Tsyplakov, 2006. "Introduction to prediction in classical time series models (in Russian)," Quantile, Quantile, issue 1, pages 3-19, September.
    • Handle: RePEc:qnt:quantl:y:2006:i:1:p:3-19
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    File URL: http://quantile.ru/01/01-AT.pdf
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    References listed on IDEAS

    as
    1. Baillie, Richard T. & Bollerslev, Tim, 1992. "Prediction in dynamic models with time-dependent conditional variances," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 91-113.
    2. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
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    Cited by:

    1. Alexander Tsyplakov, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 115-132.

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