Shrinkage Estimation and Identification of Latent Group Structures in Panel Data with Interactive Fixed Effects

Ali Mehrabani and Shahnaz Parsaeian
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Ali Mehrabani: Department of Economics, University of Kansas, Lawrence, KS 66045
Shahnaz Parsaeian: Department of Economics, University of Kansas, Lawrence, KS 66045

No 202516, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: This paper provides a framework for joint shrinkage estimation and identification of latent group structures in panel data models with interactive fixed effects and large number of explanatory variables. The latent structure of the model allows individuals to be classified into a number of groups where the number of groups and/or each individual’s group identity are unknown. A doubly penalized principal component estimation procedure using a pairwise fusion penalty and an adaptive LASSO (least absolute shrinkage and selection operator) penalty is introduced to detect the latent group structure and select the relevant regressors. To implement the proposed approach, an alternating direction method of multipliers algorithm has been developed. The proposed method is further illustrated by simulation studies and an empirical application of economic growth across various countries which demonstrate the good performance of the method.

Keywords: ADMM algorithm; high dimensionality; interactive fixed effects; pairwise adaptive group fused LASSO; parameter heterogeneity; principal component analysis. (search for similar items in EconPapers)
JEL-codes: C33 C38 C51 (search for similar items in EconPapers)
Date: 2025-11
New Economics Papers: this item is included in nep-dcm and nep-ecm
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