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The Combined Forecasts Using the Akaike Weights

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  • Mariola Pilatowska

    (Nicolaus Copernicus University in Torun)

Abstract

The focus in the paper is on the information criteria approach and especially the Akaike information criterion which is used to obtain the Akaike weights. This approach enables to receive not one best model, but several plausible models for which the ranking can be built using the Akaike weights. This set of candidate models is the basis of calculating individual forecasts, and then for combining forecasts using the Akaike weights. The procedure of obtaining the combined forecasts using the AIC weights is proposed. The performance of combining forecasts with the AIC weights and equal weights with regard to individual forecasts obtained from models selected by the AIC criterion and the a posteriori selection method is compared in simulation experiment. The conditions when the Akaike weights are worth to use in combining forecasts were indicated. The use of the information criteria approach to obtain combined forecasts as an alternative to formal hypothesis testing was recommended.

Suggested Citation

  • Mariola Pilatowska, 2009. "The Combined Forecasts Using the Akaike Weights," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 5-16.
    • Handle: RePEc:cpn:umkdem:v:9:y:2009:p:5-16
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    File URL: http://www.dem.umk.pl/dem/archiwa/v9/01_MPilatowska_UMK.pdf
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    References listed on IDEAS

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    6. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, August.
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    Cited by:

    1. Emilian Dobrescu, 2014. "A Hybrid Forecasting Approach," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(35), pages 390-390, February.

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