文章基本信息

标题：Cross-Validation Model Averaging for Generalized Functional Linear Model
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作者：Haili Zhang ; Guohua Zou
期刊名称：Econometrics
印刷版ISSN：2225-1146
出版年度：2020
卷号：8
期号：1
页码：7
DOI：10.3390/econometrics8010007
语种：English
出版社：MDPI, Open Access Journal
摘要：Functional data is a common and important type in econometrics and has been easier and easier to collect in the big data era. To improve estimation accuracy and reduce forecast risks with functional data, in this paper, we propose a novel cross-validation model averaging method for generalized functional linear model where the scalar response variable is related to a random function predictor by a link function. We establish asymptotic theoretical result on the optimality of the weights selected by our method when the true model is not in the candidate model set. Our simulations show that the proposed method often performs better than the commonly used model selection and averaging methods. We also apply the proposed method to Beijing second-hand house price data.