Homogeneity and Sparsity Analysis for High-Dimensional Panel Data Models

Bibliographic Data

ID19418175
AuthorsWu Wang (0000-0003-0563-6641, Renmin University of China), Zhongyi Zhu (0000-0003-0683-8106, Fudan University, corresponding author)
Year2024
Volume42
Issue1
Pages26-35
Publication date2024-01-02
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2022.2140667
OpenAlexW4307565345
LanguageEN
Citations received2
References cited36

In this article, we are interested in detecting latent group structures and significant covariates in a high-dimensional panel data model with both individual and time fixed effects. The slope coefficients of the model are assumed to be subject dependent, and there exist group structures where the slope coefficients are homogeneous within groups and heterogeneous between groups. We develop a penalized estimator for recovering the group structures and the sparsity patterns simultaneously. We propose a new algorithm to optimize the objective function. Furthermore, we propose a strategy to reduce the computational complexity by pruning the penalty terms in the objective function, which also improves the accuracy of group structure detection. The proposed estimator can recover the latent group structures and the sparsity patterns consistently in large samples. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies and illustrated with a real dataset

Algorithm · Covariate · Estimator · Group structure · Homogeneous · Mathematical optimization · Monte Carlo method · Panel data · Penalty method · Statistics · Computer Science · Mathematics · Spatial and Panel Data Analysis · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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