Model averaging prediction for possibly nonstationary autoregressions

Tzu-Chi Lin and Chu-An Liu

Journal of Econometrics, 2025, vol. 249, issue PB

Abstract: As an alternative to model selection (MS), this paper considers model averaging (MA) for integrated autoregressive processes of infinite order (AR(∞)). We derive a uniformly asymptotic expression for the mean squared prediction error (MSPE) of the averaging prediction with fixed weights and then propose a Mallows-type criterion to select the data-driven weights that minimize the MSPE asymptotically. We show that the proposed MA estimator and its variants, Shibata and Akaike MA estimators, are asymptotically optimal in the sense of achieving the lowest possible MSPE. We further demonstrate that MA can provide significant MSPE reduction over MS in the algebraic-decay case. These theoretical findings are extended to integrated AR(∞) models with deterministic time trends and are supported by Monte Carlo simulations and real data analysis.

Keywords: Asymptotic improvability; Asymptotic optimality; Integrated autoregressive processes; Model averaging (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:249:y:2025:i:pb:s030440762500048x

DOI: 10.1016/j.jeconom.2025.105994

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