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Dynamic Econometric Models

Combined Forecasts Using the Akaike Weights
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Combined Forecasts Using the Akaike Weights

Authors

  • Mariola Piłatowska Nicolaus Copernicus University in Toruń http://orcid.org/0000-0002-9284-7190

DOI:

https://doi.org/10.12775/DEM.2009.001

Keywords

combining forecasts, weighting schemes, information criteria

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.

References

Akaike, H. (1973), Information Theory as an Extension of the Maximum Likelihood Principle, [in:] Petrov, B. N., Csaki, F., Second International Symposium on Information Theory, Akademia Kiado, Budapest.

Akaike, H. (1978), On the Likelihood of a Time Series Model, The Statistician, 27, 217–235. DOI: http://dx.doi.org/10.2307/2988185

Armstrong, J. S. (2001), Principles of Forecasting, Springer.

Atkinson, A. C. (1980), A Note on the Generalized Information Criteria for Choice of a Model, Biometrika, 67 (2), 413–418.

Bates, J. M., Granger, C. W. J. (1969), The Combinations of Forecasts, Operations Research Quarterly, 20, 415–468. DOI: http://dx.doi.org/10.1057/jors.1969.103

Burnham, K. P., Anderson, D. R. (2002), Model Selection and Multimodel Inference, Springer.

Burnham, K. P., Anderson, D. R. (2004), Multimodel Inference. Understanding AIC and BIC in Model Selection, Sociological Methods and Research, vol. 33 (2), 261–304.

Jagannathan, R. Ma, T. (2003), Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps, The Journal of Finance, 58 (4), 1651–1684. DOI: http://dx.doi.org/10.1111/1540-6261.00580

Kapetanios, G., Labhard, V., Price, S. (2008), Forecasting using Bayesian and Informationtheoretic Model Averaging: an Application to U.K. Inflation, Journal of Business and Economics Statistics, 26 (1), 33–41.

Kitchen, J., Monaco, R. (2003), Real-Time Forecasting in Practice, Business Economics, 38 (4), 10–19.

Marcellino, M. (2004), Forecast Pooling for Short Time Series of Macroeconomic Variables, Oxford Bulletin of Economic and Statistics, 66, 91–112.

Min, C. K., Zellner, A. (1993), Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates, Journal of Econometrics, 53 (1–2), 89–118.

Stock, J. H., Watson, M. (2004), Combination Forecasts of Output Growth in a Seven-Country Data Set, Journal of Forecasting, 8, 230–251.

Stock, J. H., Watson, M. (2006), Forecasting with Many Predictors, [in:] Elliott, G., Granger, C. W. J., Timmermann, A. (ed.), Handbook of Economic Forecasting, Elsevier.

Swanson, N. R., Zeng, T. (2001), Choosing Among Competing Econometric Forecasts: Regression-based Forecast Combination using Model Selection, Journal of Forecasting, 20, 425–440.

Timmermann, A. (2006), Forecast Combinations, [in:] Elliott G., Granger C. W. J., Timmermann A. (ed.), Handbook of Economic Forecasting, ch. 4, Elsevier.

Dynamic Econometric Models

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Published

2009-07-18

How to Cite

1.
PIŁATOWSKA, Mariola. Combined Forecasts Using the Akaike Weights. Dynamic Econometric Models. Online. 18 July 2009. Vol. 9, pp. 5-16. [Accessed 7 July 2025]. DOI 10.12775/DEM.2009.001.
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Vol. 9 (2009)

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Articles

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The journal provides an Open Access to its content based on the non-exclusive licence Creative Commons (CC BY-ND 4.0).

To enable the publisher to disseminate the author's work to the fullest extent, the author must agrees to the terms and conditions of the License Agreement with Nicolaus Copernicus University.

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