Identifying Latent Structures in Panel Data

By Liangjun Su, Zhentao Shi, Peter C. B. Phillips. 2016

273 citations recorded by the source corpus.

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized regression techniques. We focus on linear models where the slope parameters are heterogeneous across groups but homogenous within a group and the group membership is unknown. Two approaches are considered -- penalized least squares (PLS) for models without endogenous regressors, and penalized GMM (PGMM) for 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 PLS 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 both in classification and estimation. An empirical application investigating the determinants of cross-country savings rates finds two latent groups among 56 countries, providing empirical confirmation that higher savings rates go in hand with higher income growth.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/382cbea00099d3914ff9e24d7cdda42bc7954fbf

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Citations in the corpus

Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, background): “…and Lee (2013), Bonhomme and Manresa (2015), Moon and Weidner (2015), Moon and Weidner (2016), Kock (2016b), Manresa (2016), Lu and Su (2016), Su et al. (2016), Moon et al. (2018), Kock and Tang (2019), Bonhomme et al. (2019a), Bonhomme et al. (2019b), Gao and Li (2019), Chen et al. (2020),…”
Estimation of Panel Group Structure Models with Structural Breaks in Group Memberships and Coefficients (2020, unknown): “For example, the panel models with common structural breaks (Qian and Su, 2016; Baltagi et al., 2017) can be regarded as a special case of (1) by imposing cross-sectional homogeneity of βgit,t.”
Nuclear norm regularized estimation of panel regression models (2018, unknown): “Alternatively, Lu and Su (2016) allow for unknown R0, but assume consistency of their estimator for β0.”
Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso (2016, unknown): “It is worth mentioning that Ke et al. (2015) and Su et al. (2014) investigate similar problems to ours.”
Determining the number of groups in latent panel structures with an application to income and democracy (2017, methodology): “We consider a latent group panel structure as recently studied by Su, Shi, and Phillips (2016), where the number of groups is unknown and has to be determined empirically.”