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Online Forecast Combination for Dependent Heterogeneous Data

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  • Sancetta, A.

Abstract

This paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and no conditions are imposed on the data sequences and the individual forecasts apart from a tail condition. The theoretical results show that the bounds are also valid in the case of time varying combination weights, under specific conditions on the nature of time variation. Some experimental evidence to confirm the results is provided.

Suggested Citation

  • Sancetta, A., 2007. "Online Forecast Combination for Dependent Heterogeneous Data," Cambridge Working Papers in Economics 0718, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:0718
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    References listed on IDEAS

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    Cited by:

    1. Assenmacher-Wesche, Katrin & Pesaran, M. Hashem, 2007. "Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows," IZA Discussion Papers 3071, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Forecast Combination; Model Selection; Multiplicative Update; Non-asymptotic Bound; On-line Learning.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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