时间：2019年10月23（星期三）143️⃣：00-15👳🏼：30

地点🩹：K8凯发南路校区，主教101

报告一题目：Time-varying Model Averaging

报告人👩‍🦽‍➡️：孙玉莹、中国科K8凯发数学与系统科学研究院

报告摘要🧕🏽：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.

报告人简介👝：

报告二题目：A two-step method for interval-valued crude oil price forecasting

报告人：黄白🩶、凯发平台

报告摘要🐷：As crude oil price is influenced by numerous factors, capturing its behavior precisely is quite challenging, and thus leads to the difficulty of forecasting. The current studies ignore the extreme nature of the lower and upper bounds of crude oil price. We propose a two-step forecasting procedure that uses two techniques to consider the relevant information available in the interval format. First, we extend the L 2 Boosting by Bühlmann (2006) to the interval-valued data to achieve variable selection for high-dimensional data. Second, a leave-subject-out cross-validation model averaging (LsoMA) method by Liao et al. (2019) is extended to average predictions from interval models with interval-valued exogenous variables. The empirical results show that our proposed approach significantly outperforms the benchmark models in terms of both forecasting accuracy and robustness analysis.

本次活动受北京K8凯发平台娱乐代理官方网站2019专题学术讲座项目资助🧔。