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Combining Nonparametric and Optimal Linear Time Series Predictions

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  • Sophie Dabo-Niang

    (MODAL - MOdel for Data Analysis and Learning - LPP - Laboratoire Paul Painlevé - UMR 8524 - Université de Lille - CNRS - Centre National de la Recherche Scientifique - Université de Lille, Sciences et Technologies - Centre Inria de l'Université de Lille - Inria - Institut National de Recherche en Informatique et en Automatique - METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694 - Université de Lille - CHRU Lille - Centre Hospitalier Régional Universitaire [CHU Lille] - Polytech Lille - École polytechnique universitaire de Lille, LPP - Laboratoire Paul Painlevé - UMR 8524 - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Christian Francq

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, IP Paris - Institut Polytechnique de Paris)

  • Jean-Michel Zakoïan

    (LFA - Laboratoire de Finance Assurance - Centre de Recherche en Économie et Statistique (CREST) - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique, EQUIPPE - Economie Quantitative, Intégration, Politiques Publiques et Econométrie - Université de Lille, Sciences et Technologies - Université de Lille, Sciences Humaines et Sociales - PRES Université Lille Nord de France - Université de Lille, Droit et Santé)

Abstract

We introduce a semiparametric procedure for more efficient prediction of a strictly stationaryprocess admitting an ARMA representation. The procedure is based on the estimation of the ARMArepresentation, followed by a nonparametric regression where the ARMA residuals are used as explanatoryvariables. Compared to standard nonparametric regression methods, the number of explanatory variablescan be reduced because our approach exploits the linear dependence of the process. We establish consistencyand asymptotic normality results. A Monte Carlo study and an empirical application on stockindices suggest that significant gains can be achieved with our approach.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Sophie Dabo-Niang & Christian Francq & Jean-Michel Zakoïan, 2010. "Combining Nonparametric and Optimal Linear Time Series Predictions," Post-Print hal-05417860, HAL.
    • Handle: RePEc:hal:journl:hal-05417860
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    2. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
    3. Ardelean, Vlad & Pleier, Thomas, 2013. "Outliers & predicting time series: A comparative study," FAU Discussion Papers in Economics 05/2013, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

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