Artikel
Cross-validation model averaging for generalized functional linear model
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 8 ; Year: 2020 ; Issue: 1 ; Pages: 1-35 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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asymptotic optimality
cross-validation
generalized functional linear model
model averaging
- Event
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Geistige Schöpfung
- (who)
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Zhang, Haili
Zou, Guohua
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2020
- DOI
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doi:10.3390/econometrics8010007
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Zhang, Haili
- Zou, Guohua
- MDPI
Time of origin
- 2020
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