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Regression-based Forecast Combination Methods

Author

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  • Wei,Xiaoqiao

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.

Suggested Citation

  • Wei,Xiaoqiao, 2009. "Regression-based Forecast Combination Methods," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-18, December.
    • Handle: RePEc:rjr:romjef:v::y:2009:i:4:p:5-18
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    More about this item

    Keywords

    forecast combinations; least squares; sequential selection; stabilization; shrinkage;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity

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