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Model averaging in predictive regressions

Author

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  • Chu‐An Liu
  • Biing‐Shen Kuo

Abstract

In this paper, we consider forecast combination in a predictive regression. We construct the point forecast by combining predictions from all possible linear regression models, given a set of potentially relevant predictors. We derive the asymptotic risk of least‐squares averaging estimators in a local asymptotic framework. We then develop a frequentist model averaging criterion, an asymptotically unbiased estimator of the asymptotic risk, to select forecast weights. Monte Carlo simulations show that our averaging estimator compares favourably with alternative methods, such as weighted AIC, weighted BIC, Mallows model averaging and jackknife model averaging. The proposed method is applied to stock return predictions.

Suggested Citation

  • Chu‐An Liu & Biing‐Shen Kuo, 2016. "Model averaging in predictive regressions," Econometrics Journal, Royal Economic Society, vol. 19(2), pages 203-231, June.
    • Handle: RePEc:wly:emjrnl:v:19:y:2016:i:2:p:203-231
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    File URL: http://hdl.handle.net/10.1111/ectj.12063
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    Cited by:

    1. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    2. In, YeonJun & Jung, Jae-Yoon, 2022. "Simple averaging of direct and recursive forecasts via partial pooling using machine learning," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1386-1399.
    3. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    4. Shutong Xie & Zongbao He & Yee Man Loh & Yu Yang & Kunhong Liu & Chao Liu & Chi Fai Cheung & Nan Yu & Chunjin Wang, 2024. "A novel interpretable predictive model based on ensemble learning and differential evolution algorithm for surface roughness prediction in abrasive water jet polishing," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2787-2810, August.
    5. Benchimol, Andrés, 2017. "Proyección de mortalidad en España mediante mixturas de modelos y análisis del impacto económico del riesgo de longevidad /Mortality Projection in Spain through Mixtures of Models and Analysis of the ," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 341-366, Mayo.
    6. Shaobo Jin & Sebastian Ankargren, 2019. "Frequentist Model Averaging in Structural Equation Modelling," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 84-104, March.
    7. Zhang, Xiaomeng & Zhang, Xinyu, 2023. "Optimal model averaging based on forward-validation," Journal of Econometrics, Elsevier, vol. 237(2).
    8. Chen, Yi-Ting & Liu, Chu-An & Su, Jiun-Hua, 2025. "Bregman model averaging for forecast combination," Journal of Econometrics, Elsevier, vol. 251(C).
    9. Chen, Yi-Ting & Liu, Chu-An, 2023. "Model averaging for asymptotically optimal combined forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 592-607.
    10. Michael Schomaker & Christian Heumann, 2020. "When and when not to use optimal model averaging," Statistical Papers, Springer, vol. 61(5), pages 2221-2240, October.
    11. Boot, Tom, 2023. "Joint inference based on Stein-type averaging estimators in the linear regression model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1542-1563.
    12. Chu-An Liu & Biing-Shen Kuo & Wen-Jen Tsay, 2017. "Autoregressive Spectral Averaging Estimator," IEAS Working Paper : academic research 17-A013, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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