主题｜Topic：Optimal Local Model Averaging for Divergent-Dimensional Functional-Coefficient Regressions
时间｜Time：10月13日（周五）10: 00-11: 30
地点｜Venue：文澴楼711教室｜Class Room 711，WENHUAN
主讲｜Speaker
洪少新，山东大学经济研究院助理研究员，主要从事计量经济学和统计学的理论和应用研究工作，具体研究方向包括非平稳时间序列分析、模型平均和高维数据统计推断等。主要工作发表于 The Econometrics Journal 和 Economics Letters 等，主持自科基金青年项目。
摘要｜Abstract
This paper proposes a novel local model averaging estimator for divergent-dimensional functional-coefficient regressions, which selects optimal functional combination weights by minimizing a local leave-h-out forward-validation criterion. It is shown that the proposed leave-h-out forward-validation model averaging (FVMA) estimator is asymptotically optimal in the sense of achieving the lowest possible local squared error loss in a class of functional model averaging estimators, which is also extended to the ultra-high dimensional framework. When correctly specified models are included in the candidate model set, the proposed FVMA asymptotically assigns all varying-weights to the correctly specified models. Furthermore, a simulation study and an empirical application highlight the merits of the proposed FVMA estimator relative to a variety of popular estimators with constant model averaging weights and model selection.
