Publication Type

Journal Article

Version

submittedVersion

Publication Date

12-2019

Abstract

We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle property so that the group-specific functional coefficients can be estimated as well as if the individuals’ membership were known. Several extensions are discussed. Simulations demonstrate excellent finite sample performance of the approach in both classification and estimation. We apply our method to study the heterogeneous trending behavior of GDP per capita across 91 countries for the period 1960-2012 and find four latent groups.

Keywords

Classifier-Lasso, Functional coefficient, Heterogeneity, Latent structure, Panel data, Penalized sieve estimation, Polynomial splines, Time-varying coefficients

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Business and Economic Statistics

Volume

37

Issue

2

First Page

334

Last Page

349

ISSN

0735-0015

Identifier

10.1080/07350015.2017.1340299

Publisher

Taylor & Francis

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1080/07350015.2017.1340299

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