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    (漢青經濟與金融高級研究院發布于:2019-11-22 17:30:36)


    本期主題: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 root-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.

    報告人:孫玉瑩助理研究員,  中國研究院數學與系統科學研究院

    時間:   11月27日(周三) 12:15

    地點:   明德主樓515教師交流室

    誠邀各位老師參加。如果您有興趣,請于11月26日前回復郵件liu.zhe AT ruc.edu.cn或電話聯系,我們將為您預留座位。聯系人:劉喆  62510919。


    Yuying Sun is an Assistant Professor at Academy of Mathematics and system Science, Chinese Academy of Science. She has earned her Ph.D. from the University of Chinese Academy of Sciences. Her work has been published at many top journals such as Journal of Econometrics, Journal of Management Science and Engineering, Journal of Systems Science & Complexity.


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