Warianty tytułu
Combining Forecasts Using the Akaike Weights
Języki publikacji
Abstrakty
W artykule uwaga jest skupiona na podejściu wykorzystującym kryteria informacyjne, a w szczególności kryterium Akaike'a, które jest wykorzystywane do wyznaczenia wag Akaike'a. Podejście to umożliwia otrzymanie nie jednego, a kilku wiarygodnych modeli, dla których można stworzyć ranking stosując wagi Akaike'a. Modele te stanowią podstawę obliczenia prognoz indywidualnych, a te z kolei służą do wyznaczenia ostatecznej prognozy kombinowanej, przy formułowaniu której wykorzystuje się wagi Akaike'a. (abstrakt oryginalny)
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. (original abstract)
Rocznik
Strony
51-62
Opis fizyczny
Twórcy
autor
- Uniwersytet Mikołaja Kopernika w Toruniu
Bibliografia
- Akaike H. (1973), Information Theory as an Extension of the Maximum Likelihood Principle, [w:] 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.
- 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.
- Burnham K. P., Anderson D. R. (2002), Model Selection and Multimodel Inference, Springer.
- Jagannathan R. Ma T. (2003), Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps, "The Journal of Finance", 58 (4), 1651-1684.
- 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, [w:] 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, [w:] Elliott G., Granger C. W. J., Timmermann A. (ed.), Handbook of Economic Forecasting, Elsevier.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171611077