State-Varying Model Averaging Prediction

Yuying Sun, Shaoxin Hong and Zongwu Cai
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Yuying Sun: School of Economics and Management, University of Chinese Academy of Sciences and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China
Shaoxin Hong: Center for Economic Research, Shandong University, Jinan, Shandong 250100, China
Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA

No 202507, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: This paper proposes a novel state-varying model averaging prediction for varyingcoefficient models that accounts for parameter uncertainty and model misspecification. We develop a leave-h-out state-dependent forward-validation criterion to select state-varying combination weights. It is shown that the proposed averaging prediction is asymptotically optimal in the sense of achieving the lowest possible out-of-sample prediction risk in a class of model averaging estimators. This complements existing model averaging methods that primarily focus on minimizing the in-sample squared error loss. Besides, when the set of candidate models includes correctly specified models, the proposed approach asymptotically assigns full weight to these models. Furthermore, the proposed approach is flexible and encompasses special cases including ultra-high dimensional models as well as state-varying factor-augmented regression models. Simulation studies and empirical applications highlight the merits of the proposed averaging prediction relative to other existing model averaging and model selection methods.

Keywords: Asymptotic optimality; Varying-coefficient models; Forward-validation; Model averaging; Weight convergence (search for similar items in EconPapers)
JEL-codes: C13 C2 (search for similar items in EconPapers)
Date: 2025-03
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