Forecasting time series of economic processes by model averaging across data frames of various lengths |
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Authors: | Nikita A. Moiseev |
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Affiliation: | Department of Mathematical Methods in Economics, Plekhanov Russian University of Economics, Moscow, Russian Federation |
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Abstract: | This paper presents an extension of mean-squared forecast error (MSFE) model averaging for integrating linear regression models computed on data frames of various lengths. Proposed method is considered to be a preferable alternative to best model selection by various efficiency criteria such as Bayesian information criterion (BIC), Akaike information criterion (AIC), F-statistics and mean-squared error (MSE) as well as to Bayesian model averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models having different number of observations and selects forecast weights by minimizing the unbiased estimator of MSFE. Proposed method also yields forecast confidence intervals with a given significance level what is not possible when applying other model averaging methods. In addition, out-of-sample simulation and empirical testing proves efficiency of such kind of averaging when forecasting economic processes. |
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Keywords: | Data frame selection Bayesian information criterion Akaike information criterion forecast combination model averaging interval forecast mean-squared forecast error model averaging |
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