Optimal model averaging for divergent-dimensional Poisson regressions

Jiahui Zou, Wendun Wang, Xinyu Zhang, Guohua Zou

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)


This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback–Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.
Original languageEnglish
Pages (from-to)775-805
Number of pages31
JournalEconometric Reviews
Issue number7
Publication statusPublished - 2022

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© 2022 The Author(s). Published with license by Taylor and Francis Group, LLC.


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