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Open AccessFeature PaperArticle

Cross-Validation Model Averaging for Generalized Functional Linear Model

by Haili Zhang 1,2 and Guohua Zou 3,*
1
University of Chinese Academy of Sciences, Beijing 100049, China
2
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
3
School of Mathematical Sciences, Capital Normal University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Econometrics 2020, 8(1), 7; https://doi.org/10.3390/econometrics8010007
Received: 2 September 2019 / Revised: 6 February 2020 / Accepted: 18 February 2020 / Published: 24 February 2020
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
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. View Full-Text
Keywords: generalized functional linear model; cross-validation; model averaging; asymptotic optimality generalized functional linear model; cross-validation; model averaging; asymptotic optimality
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Zhang, H.; Zou, G. Cross-Validation Model Averaging for Generalized Functional Linear Model. Econometrics 2020, 8, 7.

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