Model averaging for interval-valued data

主讲人：张新雨 中国科学院数学与系统科学研究院研究员

时间：2025年4月15日14：00

地点：三号楼332室

举办单位：数理学院

主讲人介绍：张新雨，中科院数学与系统科学研究院/预测中心研究员。主要从事计量经济学和统计学的理论和应用研究工作，具体研究方向包括模型平均、机器学习、组合预测和医学统计等。2010年在中科院系统所获博士学位，曾是TAMU博士后和PSU的Research Fellow。担任期刊《JSSC》领域主编、期刊《SADM》、《系统科学与数学》、《应用概率统计》等的AE或编委，是双法学会数据科学分会副理事长、国际统计学会当选会员和智源青年科学家。先后主持国家自然科学基金委优秀和杰出青年研究基金项目，曾获得中国管理学青年奖和中科院优秀博士学位论文等奖励。发表了50多篇学术论文,其中20余篇论文发表在Annals of Statistics、Biometrika、JASA、JRSSB、Journal of Econometrics和Econometric Theory。

内容介绍：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.