主講人：張新雨 中國科學院數學與系統科學研究院研究員

時間：2025年4月15日14：00

地點：三號樓332室

舉辦單位：數理學院

主講人介紹：張新雨，中科院數學與系統科學研究院/預測中心研究員。主要從事計量經濟學和統計學的理論和應用研究工作，具體研究方向包括模型平均、機器學習、組合預測和醫學統計等。2010年在中科院系統所獲博士學位，曾是TAMU博士后和PSU的Research Fellow。擔任期刊《JSSC》領域主編、期刊《SADM》、《系統科學與數學》、《應用概率統計》等的AE或編委，是雙法學會數據科學分會副理事長、國際統計學會當選會員和智源青年科學家。先后主持國家自然科學基金委優秀和杰出青年研究基金項目，曾獲得中國管理學青年獎和中科院優秀博士學位論文等獎勵。發表了50多篇學術論文,其中20余篇論文發表在Annals of Statistics、Biometrika、JASA、JRSSB、Journal of Econometrics和Econometric Theory。

內容介紹：In recent years, model averaging, by which estimates are obtained based on not one single model but a weighted ensemble of models, has received growing attention as an alternative to model selection. To-date, methods for model averaging have been developed almost exclusively for point-valued data, despite the fact that interval-valued data are commonplace in many applications and the substantial body of literature on estimation and inference methods for interval-valued data. This paper focuses on the special case of interval time series data, and assumes that the mid-point and log-range of the interval values are modelled by a two-equation vector autoregressive with exogenous covariates (VARX) model. We develop a methodology for combining models of varying lag orders based on a weight choice criterion that minimises an unbiased estimator of the squared error risk of the model average estimator. We prove that this method yields predictors of mid-points and ranges with an optimal asymptotic property. In addition, we develop a method for correcting the range forecasts, taking into account the forecast error variance. An extensive simulation experiment examines the performance of the proposed model averaging method in finite samples. We apply the method to an interval-valued data series on crude oil future prices.