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Jackknife model averaging for expectile regressions in increasing dimension

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  • Tu, Yundong
  • Wang, Siwei

Abstract

Expectile regression is a useful tool for modeling data with heterogeneous conditional distributions. This paper develops the jackknife model averaging method for expectile regressions. The asymptotic properties of expectile estimator under misspecification with increasing dimension of parameters have been studied. The model averaging expectile estimator using the leave-one-out cross-validated weight is shown to be asymptotically optimal in the sense of out-of-sample final prediction error. Numerical results demonstrate the nice performance of the averaging estimators.

Suggested Citation

  • Tu, Yundong & Wang, Siwei, 2020. "Jackknife model averaging for expectile regressions in increasing dimension," Economics Letters, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:ecolet:v:197:y:2020:i:c:s0165176520303670
    DOI: 10.1016/j.econlet.2020.109607
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    References listed on IDEAS

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    Cited by:

    1. Yundong Tu & Siwei Wang, 2023. "Variable Screening and Model Averaging for Expectile Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 574-598, June.
    2. Zhang, Xiaomeng & Zhang, Xinyu, 2023. "Optimal model averaging based on forward-validation," Journal of Econometrics, Elsevier, vol. 237(2).

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    More about this item

    Keywords

    Expectile regression; Heteroscedasticity; Jackknife model averaging; High dimensional data;
    All these keywords.

    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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