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 membership is unknown. Two approaches are considered—penalized 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.
2215-2264
Su, Liangjun
e1137c6b-c51a-4408-b4b9-bfb3f26f7955
Shi, Zhentao
157ef919-197f-4e66-9be8-8f75716d3430
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
November 2017
Su, Liangjun
e1137c6b-c51a-4408-b4b9-bfb3f26f7955
Shi, Zhentao
157ef919-197f-4e66-9be8-8f75716d3430
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
Su, Liangjun, Shi, Zhentao and Phillips, Peter C.B.
(2017)
Identifying latent structures in panel data.
Econometrica, 84 (6), .
(doi:10.3982/ECTA12560).
Abstract
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 considered—penalized 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.
Text
panel_structure20151229
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Accepted/In Press date: 31 December 2015
e-pub ahead of print date: 9 November 2016
Published date: November 2017
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Economics
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Local EPrints ID: 408186
URI: http://eprints.soton.ac.uk/id/eprint/408186
ISSN: 0012-9682
PURE UUID: 5ce9057d-8b63-49f1-89c7-21cb7066e729
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Date deposited: 16 May 2017 04:02
Last modified: 15 Mar 2024 12:45
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Author:
Liangjun Su
Author:
Zhentao Shi
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