Xiaorong Yang, Jia Chen, Degui Li, Runze Li
arXiv 23 Mar 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2023) · 93 citations (OpenAlex)
arXiv:2303.13218 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, J (2019) Estimating latent group structure in time-varying coefficient panel data models self | 0.950 | 7 | 6 | 86% |
| 2 | Kato, K., A. F. Galvao Jr, and G. V. Montes-Rojas (2012) Asymptotics for panel quantile regression models with individual effects | 0.909 | 8 | 4 | 75% |
| 3 | Cai, Z. and X. Xu (2008) Nonparametric quantile estimations for dynamic smooth coefficient models | 0.843 | 5 | 4 | 60% |
| 4 | Chen, L (2021) Nonparametric quantile regressions for panel data models with large $T$ | 0.843 | 5 | 3 | 60% |
| 5 | Su, L., X. Wang, and S. Jin (2019) Sieve estimation of time-varying panel data models with latent structures | 0.843 | 3 | 3 | 100% |
| 6 | Vogt, M. and O. Linton (2020) Multiscale clustering of nonparametric regression curves | 0.843 | 3 | 3 | 100% |
| 7 | Chen, J., D. Li, L. Wei, and W. Zhang (2021) Nonparametric homogeneity pursuit in functional-coefficient models self | 0.644 | 2 | 2 | 100% |
| 8 | Galvao, A. F., J. Gu, and S. Volgushev (2020) On the unbiased asymptotic normality of quantile regression with fixed effects | 0.644 | 2 | 2 | 100% |
| 9 | Galvao, A. F. and K. Kato (2016) Smoothed quantile regression for panel data | 0.644 | 2 | 2 | 100% |
| 10 | Vogt, M. and O. Linton (2017) Classification of non-parametric regression functions in longitudinal data models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 65 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimation of Grouped Time-Varying Network Vector Autoregression Models | 0.405 | 1 | 1 |