Econometrica: Nov 2016, Volume 84, Issue 6

Identifying Latent Structures in Panel Data

DOI: 10.3982/ECTA12560
p. 2215-2264

Liangjun Su, Zhentao Shi, Peter C. B. Phillips

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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Supplement to "Identifying Latent Structures in Panel Data"

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Supplement to "Identifying Latent Structures in Panel Data"

This supplement is composed of four parts. Section S1 contains the proofs of some technical lemmas for the proofs of the main results in Section 2.  Section S2 gives bias correction formulae in linear panel data models for both PPL and PGMM estimation. Sections S3 and S4 contain some additional simulation and applications results, respectively.

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