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Model Averaging for Asymptotically Optimal Combined Forecasts

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Abstract

We propose a model-averaging (MA) method for constructing asymptotically optimal combined forecasts. The asymptotic optimality is defined in terms of approximating an unknown conditional-mean sequence based on the local-to-zero asymptotics. Unlike existing methods, our method is designed for combining a set of forecast sequences, which is more general than combining a set of single forecasts, generated from a set of predictive regressions. This design generates essential features that are not shared by related existing methods, and the resulting asymptotically optimal weights may be consistently estimated under suitable conditions. We also assess the forecasting performance of our method using simulation data and real data.

Suggested Citation

  • Yi-Ting Chen & Chu-An Liu, 2021. "Model Averaging for Asymptotically Optimal Combined Forecasts," IEAS Working Paper : academic research 21-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    • Handle: RePEc:sin:wpaper:21-a002
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    Cited by:

    1. is not listed on IDEAS
    2. Chen, Yi-Ting & Liu, Chu-An & Su, Jiun-Hua, 2025. "Bregman model averaging for forecast combination," Journal of Econometrics, Elsevier, vol. 251(C).
    3. John Buckell & Alice Wreford & Matthew Quaife & Thomas O. Hancock, 2025. "A break from the norm? Parametric representations of preference heterogeneity for discrete choice models in health," Papers 2506.14099, arXiv.org.

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    Keywords

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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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