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TIME-VARYING COMPLETE SUBSET AVERAGING IN A DATA-RICH ENVIRONMENT

Published online by Cambridge University Press:  21 March 2025

Haiqi Li,
Jing Zhang,
Xingyi Chen and
Yongmiao Hong
Haiqi Li
Affiliation:
Hunan University
Jing Zhang*
Affiliation:
University of Jinan
Xingyi Chen
Affiliation:
Xiamen University
Yongmiao Hong
Affiliation:
Chinese Academy of Sciences and University of Chinese Academy of Sciences
*
Address correspondence to Jing Zhang, Business School, University of Jinan, Jinan, China, e-mail: jingzhang00@hnu.edu.cn.
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Abstract

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This study proposes two novel time-varying model-averaging methods for time-varying parameter regression models. When the number of predictors is small, we propose a novel time-varying complete subset-averaging (TVCSA) procedure, where the optimal time-varying subset size is obtained by minimizing the local leave-h-out cross-validation criterion. The TVCSA method is asymptotically optimal for achieving the lowest possible local mean squared error. When the number of predictors is relatively large, we propose a factor TVCSA method to reduce the computational burden by first reducing the dimension of predictors by extracting a few factors using principal component analysis and then obtaining the TVCSA forecasts from time-varying models with the generated factors. We show that the TVCSA estimator remains asymptotically optimal in the presence of generated factors. Monte Carlo simulation studies have provided favorable evidence for the TVCSA methods relative to the popular model-averaging methods in the literature. Empirical applications to equity premiums and inflation forecasting highlight the practical merits of the proposed methods.

Information

Type
ARTICLE
Information
Econometric Theory , Volume 42 , Issue 3 , June 2026, pp. 548 - 603
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Footnotes

We thank Peter C. B. Phillips (Editor), Robert Taylor (Co-Editor), three referees for their comments and suggestions. Li thanks the support of the National Natural Science Foundation of China (NSFC) (Project No. 72171076). Zhang thanks the support of Shandong Provincial Natural Science Foundation of China for Young Scholars (Project No. ZR2024QG138) and the Higher Education Institution Youth Entrepreneurship Team Plan of Shandong Province (Project No. 2024KJB007). Hong thanks the support of the Basic Scientific Center Project entitled as “Econometric Modeling and Economic Policy Studies” of NSFC (Project No. 71988101).

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