Regression-based Forecast Combination Methods

Xiaoqiao Wei

Journal for Economic Forecasting, 2009, issue 4, 5-18

Abstract: 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.

Keywords: forecast combinations; least squares; sequential selection; stabilization; shrinkage (search for similar items in EconPapers)
JEL-codes: C32 E24 (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:rjr:romjef:v::y:2009:i:4:p:5-18

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