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Cited by 265

The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.

Identifying Latent Structures in Panel Data

Su, Liangjun, Shi, Zhentao, Phillips, Peter C. B.·2016·Econometrica
DOI 10.3982/ecta12560
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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.

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Metadata

Authors
Su, L., Shi, Z., & Phillips, P. C. B.
Venue
Econometrica
Publisher
The Econometric Society
Published
2016
Volume
84
Issue
6
Pages
2215-2264
ISSN
0012-9682 (ISSN-L)
DOI
10.3982/ecta12560
OpenAlex
W3121461680
Cited by
265
References
73
FWCI
13.96
Type
journal article
Language
en
Open access
closed

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