Abstract
This paper presents a new ordinary least squares model averaging method which is proposed to be a preferable alternative to Mallows Model Averaging(MMA), Bayesian Model Averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models and selects forecast weights by minimizing the unbiased estimator of mean-squared forecast error (MSFE). Proposed method also yields forecast confidence intervals with given significance level what is not possible when applying other model averaging methods. In addition out-of-sample simulation and empirical testing proves the supremacy of MSFE model averaging over existing combination approaches.
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