Time-varying model averaging
| Autor: | Yuying Sun, Shouyang Wang, Xinyu Zhang, Yongmiao Hong, Tae-Hwy Lee |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Economics and Econometrics
Mathematical optimization Mean squared error Series (mathematics) Applied Mathematics Model selection 05 social sciences Estimator 01 natural sciences Set (abstract data type) 010104 statistics & probability Asymptotically optimal algorithm 0502 economics and business 0101 mathematics Constant (mathematics) Jackknife resampling 050205 econometrics Mathematics |
| Zdroj: | Journal of Econometrics. 222:974-992 |
| ISSN: | 0304-4076 |
| Popis: | 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 T h -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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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