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June 2016 Structure identification in panel data analysis
Yuan Ke, Jialiang Li, Wenyang Zhang
Ann. Statist. 44(3): 1193-1233 (June 2016). DOI: 10.1214/15-AOS1403

Abstract

Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. While the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the groups in panel data with interactive effects induced by latent variables. It is assumed that the impact of a covariate is the same within each group, but different between the groups. An EM based algorithm is proposed to estimate the unknown parameters, and a binary segmentation based algorithm is proposed to detect the grouping. We then establish asymptotic theories to justify the proposed estimation, grouping method, and the modelling idea. Simulation studies are also conducted to compare the proposed method with the existing approaches, and the results obtained favour our method. Finally, the proposed method is applied to analyse a data set about income dynamics, which leads to some interesting findings.

Citation

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Yuan Ke. Jialiang Li. Wenyang Zhang. "Structure identification in panel data analysis." Ann. Statist. 44 (3) 1193 - 1233, June 2016. https://doi.org/10.1214/15-AOS1403

Information

Received: 1 May 2015; Revised: 1 September 2015; Published: June 2016
First available in Project Euclid: 11 April 2016

zbMATH: 1341.62214
MathSciNet: MR3485958
Digital Object Identifier: 10.1214/15-AOS1403

Subjects:
Primary: 62F08
Secondary: 62F12

Keywords: Binary segmentation algorithm , EM algorithm , homogeneity , interactive effects , panel data

Rights: Copyright © 2016 Institute of Mathematical Statistics

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Vol.44 • No. 3 • June 2016
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