Cross-validation for selecting the penalty factor in least squares model averaging

Fang Fang, Qiwei Yang and Wenling Tian

Economics Letters, 2022, vol. 217, issue C

Abstract: Asymptotic properties of least squares model averaging have been discussed in the literature under two different scenarios: (i) all candidate models are under-fitted; and (ii) the candidate models include the true model and may also include over-fitted ones. The penalty factor ϕn in the weight selection criterion plays a critical role. Roughly speaking, ϕn=2 is usually preferred in the first scenario but it does not achieve asymptotic optimality in the second scenario as ϕn=log(n) does. It is difficult in the practice to select an appropriate penalty factor since the true scenario is unknown. We propose a non-trivial cross-validation procedure to select the penalty factor that leads to an asymptotically optimal estimator in an adaptive fashion for both scenarios.

Keywords: Cross-validation; Frequentist model averaging; Linear models; Mallows model averaging (search for similar items in EconPapers)
JEL-codes: C1 C5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:217:y:2022:i:c:s0165176522002300

DOI: 10.1016/j.econlet.2022.110683

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