Estimating Latent Group Structure in Time-Varying Coefficient Panel Data Models

Jia Chen

Discussion Papers from Department of Economics, University of York

Abstract: This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite sample properties of the proposed clustering method as well as the post-clustering estimation of the group- specific time-varying coefficients. The simulation results show that our methods give comparable performance as the penalised-sieve-estimation based classifier Lasso approach by Su et al. (2018), but are computationally easier. An application to a cross-country growth study is also provided.

Keywords: Hierarchical agglomerative clustering; Generalised information criterion; Kernel estimation; Panel data; Time-varying coefficients. (search for similar items in EconPapers)
JEL-codes: C13 C14 C23 (search for similar items in EconPapers)
Date: 2018-10
New Economics Papers: this item is included in nep-ecm
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Journal Article: Estimating latent group structure in time-varying coefficient panel data models (2019)
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