Time-varying Model Averaging

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.