Time-varying Model Averaging

Yongmiao Hong, Tae Hwy Lee, Yuying Sun (), Shouyang Wang () and Xinyu Zhang ()
Additional contact information
Yuying Sun: Chinese Academy of Sciences
Shouyang Wang: Chinese Academy of Sciences
Xinyu Zhang: Chinese Academy of Sciences

No 202001, Working Papers from University of California at Riverside, Department of Economics

Abstract: Structural changes often occur in economics and finance due to changes in preferences, technologies, institutional arrangements, policies, crises, etc. Improving forecast accuracy of economic time series with structural changes is a long-standing problem. Model averaging aims at providing an insurance against selecting a poor forecast model. All existing model averaging approaches in the literature are designed with constant (non-time-varying) combination weights. Little attention has been paid to time-varying model averaging, which is more realistic in economics under structural changes. This paper proposes a novel model averaging estimator which selects optimal time-varying combination weights by minimizing a local jackknife criterion. It is shown that the proposed time-varying jackknife model averaging (TVJMA) estimator is asymptotically optimal in the sense of achieving the lowest possible local squared error loss in a class of time-varying model averaging estimators. Under a set of regularity assumptions, the TVJMA estimator is \sqrt{Th}-consistent. A simulation study and an empirical application highlight the merits of the proposed TVJMA estimator relative to a variety of popular estimators with constant model averaging weights and model selection.

Keywords: Asymptotic optimality; Forecast combination; Local stationarity; Model averaging; Structural change; Time-varying model averaging (search for similar items in EconPapers)
JEL-codes: C13 C2 (search for similar items in EconPapers)
Pages: 58 Pages
Date: 2017-11
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Forthcoming in Journal of Econometrics

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Journal Article: Time-varying model averaging (2021)
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