讲座题目：Unified Time-varying Model Averaging for General Loss Functions
内容摘要：We propose a unified time-varying model averaging approach that accommodates general loss functions, including Lin-lin loss and asymmetric squared error loss, to improve prediction performance under structural change. This flexibility enables averaging across diverse candidate models, such as time-varying coefficient quantile regression models. We develop a local forward-validation criterion to determine time-varying combination weights without the standard constraint of summing up to 1 and establish theoretical justifications previously unexplored in the literature. First, when all candidate models are misspecified, the proposed averaging prediction is asymptotically optimal in the sense of achieving the lowest possible prediction risk with a convergence rate. Second, we establish a novel convergence rate for time-varying weight consistency that does not depend on the extent of misspecification among the candidate models. Furthermore, we develop a time-varying sparsity-oriented importance learning procedure that consistently identifies the true predictor set. Monte Carlo simulations and empirical applications demonstrate superior finite-sample performance relative to existing model selection and averaging methods.
主讲人简介：崔逸凡，浙江大学长聘副教授（研究员），博士生导师。北卡罗来纳大学教堂山分校统计与运筹专业博士，曾任宾夕法尼亚大学沃顿商学院博士后研究员、新加坡国立大学统计与数据科学系助理教授。国家级青年人才计划入选者（2021）。
报告时间：2025年12月2日(周二)，16:30-18:00
线下地点：厦门大学经济楼C108
线上地点：腾讯会议 ID：246 184 969
