Evidence map›Paper›PMID 40175690›Full record

ArticleScientific reports2025

Estimation of common breaks in linear panel data models via screening and ranking algorithm.

Fuxiao Li, Yanting Xiao, Zhanshou Chen

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Fuxiao LiDepartment of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, China. fx_lmzq@163.com.
Yanting XiaoDepartment of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, China.
Zhanshou ChenSchool of Mathematics and Statistics, Qinghai Normal University, Xining, 810008, China.

Funding

National Natural Science Foundation of China 11801438National Natural Science Foundation of China 12161072National Natural Science Foundation of China 12171388Natural Science Basic Research Program of Shaanxi Province 2023-JC-YB-058
6 · The paper itself

Abstract

In this paper, we consider the estimation of common breaks for linear panel data models by means of screening and ranking algorithm. For static and dynamic panel data models, we estimate the regression coefficients using covariance estimation and generalized method of moments, respectively, and apply a screening and ranking algorithm on this basis. The possible break points are first screened by constructing local statistics based on the coefficient estimators, then further screened by the thresholding rule, and finally the final break points are screened by the information criterion. Monte Carlo simulations demonstrate that the proposed methods work well in finite samples. We apply the screening and ranking algorithm to study the influence of rural residents' consumption demand on China's economic growth using a panel of 31 provinces from 2005 to 2023 and find a break point in the model.

Indexed as

Estimation of break pointsInformation criterionLinear panel data modelsLocal statisticScreening and ranking algorithm

Identifiers

PMID40175690
PMCPMC11965510

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