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Model averaging based on leave-subject-out cross-validation

Author

Listed:
  • Gao, Yan
  • Zhang, Xinyu
  • Wang, Shouyang
  • Zou, Guohua

Abstract

This paper develops a frequentist model averaging method based on the leave-subject-out cross-validation. This method is applicable not only to averaging longitudinal data models, but also to averaging time series models which can have heteroscedastic errors. The resulting model averaging estimators are proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors. Both simulation study and empirical example show the superiority of the proposed estimators over their competitors.

Suggested Citation

  • Gao, Yan & Zhang, Xinyu & Wang, Shouyang & Zou, Guohua, 2016. "Model averaging based on leave-subject-out cross-validation," Journal of Econometrics, Elsevier, vol. 192(1), pages 139-151.
  • Handle: RePEc:eee:econom:v:192:y:2016:i:1:p:139-151
    DOI: 10.1016/j.jeconom.2015.07.006
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhao, Shangwei & Zhou, Jianhong & Li, Hongjun, 2016. "Model averaging with high-dimensional dependent data," Economics Letters, Elsevier, vol. 148(C), pages 68-71.
    2. Shou-Yung Yin & Chu-An Liu & Chang-Ching Lin, 2016. "Focused Information Criterion and Model Averaging for Large Panels with a Multifactor Error Structure," IEAS Working Paper : academic research 16-A016, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    3. repec:taf:gnstxx:v:30:y:2018:i:1:p:125-144 is not listed on IDEAS

    More about this item

    Keywords

    Asymptotic optimality; Leave-subject-out cross-validation; Longitudinal data; Model averaging; Time series;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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