Yang, Xiaorong, Chen, Jia orcid.org/0000-0002-2791-2486, Li, Degui orcid.org/0000-0001-6802-308X et al. (1 more author) (2023) Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure. Journal of Business and Economic Statistics. ISSN 0735-0015
Abstract
This paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional coefficients, a classic agglomerative clustering algorithm is used to estimate the unknown group structure and an easy-to-implement ratio criterion is proposed to determine the group number. The estimated group number and structure are shown to be consistent. Furthermore, a post-grouping local linear smoothing method is introduced to estimate the group-specific functional coefficients, and the relevant asymptotic normal distribution theory is derived with a normalisation rate comparable to that in the literature. The developed methodologies and theory are verified through a simulation study and showcased with an application to house price data from UK local authority districts, which reveals different homogeneity structures at different quantile levels.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 American Statistical Association. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Mathematics (York) The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Depositing User: | Pure (York) |
Date Deposited: | 20 Oct 2023 11:40 |
Last Modified: | 31 Oct 2024 01:06 |
Published Version: | https://doi.org/10.1080/07350015.2023.2277172 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1080/07350015.2023.2277172 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204452 |
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