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Cross-Validation Model Averaging for Generalized Functional Linear Model

Haili Zhang () and Guohua Zou ()
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Haili Zhang: University of Chinese Academy of Sciences, Beijing 100049, China
Guohua Zou: School of Mathematical Sciences, Capital Normal University, Beijing 100048, China

Econometrics, 2020, vol. 8, issue 1, 1-35

Abstract: 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.

Keywords: generalized functional linear model; cross-validation; model averaging; asymptotic optimality (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2020
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