Work

Incorporating Prior Information in Latent Structures Identification for Panel Data Models

Year: 2025

Type: article

Abstract: 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.

Source: Mathematics

DOI: 10.3390/math13091505

Authors: Yi Li, Xingxing Luo, Mengqi Liao

Institutions: Hunan Normal University, Chongqing University of Science and Technology, Xiamen University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Natural Science Foundation of Hunan Province