Publication Type

Working Paper

Version

publishedVersion

Publication Date

11-2016

Abstract

This paper studies estimation of a panel data model with latent structures where individuals can be classified into different groups where slope parameters are homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel-CARDS which is a systematic extension of the CARDS procedure proposed by Ke, Fan, and Wu (2015) in a cross section framework. The extension addresses the problem of comparing vector coefficients in a panel model for homogeneity and introduces a new concept of controlled classification of multidimensional quantities called the segmentation net. We show that the Panel-CARDS method identifies group structure asymptotically and consistently estimates model parameters at the same time. External information on the minimum number of elements within each group is not required but can be used to improve the accuracy of classification and estimation in finite samples. Simulations evaluate performance and corroborate the asymptotic theory in several practical design settings. Two empirical economic applications are considered: one explores the effect of income on democracy by using cross-country data over the period 1961-2000; the other examines the effect of minimum wage legislation on unemployment in 50 states of the United States over the period 1988-2014. Both applications reveal the presence of latent groupings in these panel data.

Keywords

CARDS, Clustering, Heterogeneous slopes, Income and democracy, Minimum wage and employment, Oracle estimator, Panel structure model

Discipline

Econometrics | Income Distribution

Research Areas

Econometrics

First Page

1

Last Page

57

Identifier

10.2139/ssrn.2881906

Publisher

Yale University, Cowles Foundation Discussion Paper No. 2063

City or Country

New Haven, CN

Copyright Owner and License

Authors

Comments

Published in Journal of Applied Econometrics, 2018, 33 (6), 797-815, https://doi.org/10.1002/jae.2632

Additional URL

https://doi.org/10.2139/ssrn.2881906

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