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Post-averaging inference for optimal model averaging estimator in generalized linear models

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  • Dalei Yu
  • Heng Lian
  • Yuying Sun
  • Xinyu Zhang
  • Yongmiao Hong

Abstract

This article considers the problem of post-averaging inference for optimal model averaging estimators in a generalized linear model (GLM). We establish the asymptotic distributions of optimal model averaging estimators for GLMs. The asymptotic distributions of the model averaging estimators are nonstandard, depending on the configuration of the penalty term in the weight choice criterion. We also propose a feasible simulation-based confidence interval estimator and investigate its asymptotic properties rigorously. Monte Carlo simulations verify the usefulness of our theoretical results, and the proposed methods are employed to analyze a stock car racing dataset.

Suggested Citation

  • Dalei Yu & Heng Lian & Yuying Sun & Xinyu Zhang & Yongmiao Hong, 2024. "Post-averaging inference for optimal model averaging estimator in generalized linear models," Econometric Reviews, Taylor & Francis Journals, vol. 43(2-4), pages 98-122, April.
    • Handle: RePEc:taf:emetrv:v:43:y:2024:i:2-4:p:98-122
    DOI: 10.1080/07474938.2023.2292377
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