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Complete subset averaging approach for high-dimensional generalized linear models

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  • Chen, Xingyi
  • Li, Haiqi
  • Zhang, Jing

Abstract

This study proposes a novel complete subset averaging (CSA) method for high-dimensional generalized linear models based on a penalized Kullback–Leibler (KL) loss. All models under consideration can be potentially misspecified, and the dimension of covariates is allowed to diverge to infinity. The uniform convergence rate and asymptotic normality of the proposed estimator are established. Moreover, it is asymptotically optimal in terms of achieving the lowest KL loss. To ease the computational burden, we randomly draw a fixed number of subsets from the complete subsets and show their asymptotic equivalence. The Monte Carlo simulation and empirical application demonstrate that the proposed CSA method outperforms popular model-averaging methods.

Suggested Citation

  • Chen, Xingyi & Li, Haiqi & Zhang, Jing, 2023. "Complete subset averaging approach for high-dimensional generalized linear models," Economics Letters, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:ecolet:v:226:y:2023:i:c:s016517652300109x
    DOI: 10.1016/j.econlet.2023.111084
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    References listed on IDEAS

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    More about this item

    Keywords

    Asymptotic optimality; Complete subset averaging; Kullback–Leibler loss; Generalized linear models;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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