Identifying latent structures in panel data

Identifying latent structures in panel data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membershi...

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Bibliographic Details
Main Authors: SU, Liangjun, SHI, Zhentao, Peter C. B. PHILLIPS
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Classification
cluster analysis
dynamic panel
group Lasso
high dimensionality
nonlinear panel
oracle property
panel structure model
parameter heterogeneity
penalized least squares
penalized GMM
penalized profile likelihood
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/1911
https://ink.library.smu.edu.sg/context/soe_research/article/2910/viewcontent/IdentifyingLatentStructuresPanelData_2014Jul_pp.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are consideredpenalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. C-Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C-Lasso also achieves the oracle property so that group-specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C-Lasso is preserved in some special cases. Simulations demonstrate good finite-sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.

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