Projects per year
Abstract
Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by pooling the information of individuals within each group. However, the currently assumed parametric and semiparametric relationship between the response and predictors may be misspecified, which leads to a wrong grouping result, and the nonparametric approach hence can be considered to avoid such mistakes. Moreover, the response may depend on predictors in different ways at various quantile levels, and the corresponding grouping structure may also vary. To tackle these problems, this paper proposes a nonparametric quantile regression method for homogeneity pursuit, and a pairwise fused penalty is used to automatically select the number of groups. The asymptotic properties are established, and an ADMM algorithm is also developed. The finite sample performance is evaluated by simulation experiments, and the usefulness of the proposed methodology is further illustrated by an empirical example.
Original language | English |
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Journal | Journal of Business & Economic Statistics |
Online published | 10 Oct 2022 |
DOIs | |
Publication status | Online published - 10 Oct 2022 |
Funding
Lian’s research is partially supported by the Hong Kong Research Grant Council (GRF grants 11300721 and 11311822). Li’s research is partially supported by the Hong Kong Research Grant Council (GRF grants 17306519, 17305319 and 17306121) and the National Social Science Fund of China (grant No. 72033002).
Research Keywords
- Homogeneity pursuit
- Nonparametric approach
- Oracle property
- Panel data model
- Quantile regression
- VARIABLE SELECTION
- GROUPED PATTERNS
Fingerprint
Dive into the research topics of 'Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models'. Together they form a unique fingerprint.Projects
- 2 Active
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GRF: Low-rank Nonparametric Regression and Application to Reinforcement Learning
LIAN, H. (Principal Investigator / Project Coordinator)
1/01/23 → …
Project: Research
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GRF: Distributed Estimation with Random Projection in Reproducing Kernel Hilbert Spaces
LIAN, H. (Principal Investigator / Project Coordinator)
1/01/22 → …
Project: Research