【主讲】孙玉莹 (中国科学院数学与系统科学研究院)
【主题】Time-Varying Model Averaging
【时间】2019年1月4日 (周五) 15：30-17：00
【地点】上海财经大学经济学院楼701室
【语言】英文
【摘要】Structural changes often occur in economics and finance due to changes in preferences,technologies, institutional arrangements, policies, crises, etc. Improving the forecast accuracy of economic time series with the evolutionary behavior is a long-standing problem.Model averaging aims at providing an insurance against selecting a poor model. All existingmodel averaging approaches are designed with constant (non-time-varying) weights. Littleattention has been paid to the time-varying model averaging, which is more realistic in economics under structural changes. This paper proposes a novel model averaging estimatorwhich selects the smoothly time-varying weights by minimizing a local jackknife criterion.It is shown that the proposed time-varying jackknife model averaging (TJMA) estimator isasymptotically optimal in the sense of achieving the lowest possible local squared errors in aclass of time-varying model averaging estimators, with allowing non-spherical errors. Undersome regularity assumptions, the TJMA estimator is root-Th consistent. A simulation studyand empirical application highlight the merits of the proposed TJMA estimator relative toa variety of popular estimators from constant model averaging and model selection.
