Published in Romanian Journal of Economic Forecasting, 2009, volume 12 issue 4, 5-18
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Least squares combinations (Granger & Ramanathan, 1984) are an important development in the forecast combination literature. However, ordinary least squares methods often perform poorly in real application due to the variability of coefficient/weight estimations. In this work, on one hand, we propose sequential subset selections to reduce the variability during combinations. On the other hand, we propose a novel method to simultaneously stabilize and shrink the coefficient/weights estimates. The proposed methods can be applied to various combination methods to improve prediction as long as their weights are determined based on ordinary least squares.
forecast combinations, least squares, sequential selection,