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Forecasting 1 to h steps ahead using partial least squares

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  • Franses, Ph.H.B.F.

Abstract

This paper proposes a methodology to jointly generate optimal forecasts from an autoregression of order p for 1 to h steps ahead. The relevant model is a Partial Least Squares Autoregression, which is positioned in between a single AR(p) model for all forecast horizons and different AR models for different horizons. Representation, estimation and forecasting using the new model are discussed. An illustration for US industrial production shows the merits of the methodology.

Suggested Citation

  • Franses, Ph.H.B.F., 2006. "Forecasting 1 to h steps ahead using partial least squares," Econometric Institute Research Papers EI 2006-47, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:8093
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    References listed on IDEAS

    as
    1. Franses, Philip Hans & Paap, Richard, 2004. "Periodic Time Series Models," OUP Catalogue, Oxford University Press, number 9780199242030.
    2. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    3. R. Bhansali, 1996. "Asymptotically efficient autoregressive model selection for multistep prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(3), pages 577-602, September.
    4. Kang, In-Bong, 2003. "Multi-period forecasting using different models for different horizons: an application to U.S. economic time series data," International Journal of Forecasting, Elsevier, vol. 19(3), pages 387-400.
    5. Weiss, Andrew A., 1991. "Multi-step estimation and forecasting in dynamic models," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 135-149.
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