Incorporating Prior Information in Latent Structures Identification for Panel Data Models

In this paper, we explore the latent structures for panel data models in presence of available prior information. The latent structure in panel models allows individuals to be classified into several distinct groups, where the individuals within the same group share the same slope parameters, while...

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Main Authors: Yi Li, Xingxing Luo, Mengqi Liao
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/13/9/1505
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author Yi Li
Xingxing Luo
Mengqi Liao
author_facet Yi Li
Xingxing Luo
Mengqi Liao
author_sort Yi Li
collection DOAJ
description In this paper, we explore the latent structures for panel data models in presence of available prior information. The latent structure in panel models allows individuals to be classified into several distinct groups, where the individuals within the same group share the same slope parameters, while the group-specific parameters are heterogeneous. To incorporate the prior information, we design a new alternating direction method of multipliers (ADMM) algorithm based on the pairwise group fused Lasso penalty approach. The asymptotic properties and the convergence of ADMM algorithm are well established. Simulation studies demonstrate the advantages of the proposed method over existing methods in terms of both estimation efficiency and detection accuracy. We illustrate the practical utility of the proposed procedure by analyzing the relationship between electricity consumption and GDP in China.
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institution Kabale University
issn 2227-7390
language English
publishDate 2025-05-01
publisher MDPI AG
record_format Article
series Mathematics
spelling doaj-art-cbd019ae12234ea8a157f402972f1c612025-08-20T03:49:22ZengMDPI AGMathematics2227-73902025-05-01139150510.3390/math13091505Incorporating Prior Information in Latent Structures Identification for Panel Data ModelsYi Li0Xingxing Luo1Mengqi Liao2College of Tourism, Hunan Normal University, Changsha 410081, ChinaSchool of Management, Chongqing University of Science and Technology, Chongqing 401331, ChinaSchool of Economics, Xiamen University, Xiamen 361005, ChinaIn this paper, we explore the latent structures for panel data models in presence of available prior information. The latent structure in panel models allows individuals to be classified into several distinct groups, where the individuals within the same group share the same slope parameters, while the group-specific parameters are heterogeneous. To incorporate the prior information, we design a new alternating direction method of multipliers (ADMM) algorithm based on the pairwise group fused Lasso penalty approach. The asymptotic properties and the convergence of ADMM algorithm are well established. Simulation studies demonstrate the advantages of the proposed method over existing methods in terms of both estimation efficiency and detection accuracy. We illustrate the practical utility of the proposed procedure by analyzing the relationship between electricity consumption and GDP in China.https://www.mdpi.com/2227-7390/13/9/1505latent structurespanel dataprior informationADMM algorithm
spellingShingle Yi Li
Xingxing Luo
Mengqi Liao
Incorporating Prior Information in Latent Structures Identification for Panel Data Models
Mathematics
latent structures
panel data
prior information
ADMM algorithm
title Incorporating Prior Information in Latent Structures Identification for Panel Data Models
title_full Incorporating Prior Information in Latent Structures Identification for Panel Data Models
title_fullStr Incorporating Prior Information in Latent Structures Identification for Panel Data Models
title_full_unstemmed Incorporating Prior Information in Latent Structures Identification for Panel Data Models
title_short Incorporating Prior Information in Latent Structures Identification for Panel Data Models
title_sort incorporating prior information in latent structures identification for panel data models
topic latent structures
panel data
prior information
ADMM algorithm
url https://www.mdpi.com/2227-7390/13/9/1505
work_keys_str_mv AT yili incorporatingpriorinformationinlatentstructuresidentificationforpaneldatamodels
AT xingxingluo incorporatingpriorinformationinlatentstructuresidentificationforpaneldatamodels
AT mengqiliao incorporatingpriorinformationinlatentstructuresidentificationforpaneldatamodels