Identifying latent group structures in spatial dynamic panels

Liangjun Su (), Wuyi Wang and Xingbai Xu

Journal of Econometrics, 2023, vol. 235, issue 2, 1955-1980

Abstract: This paper considers the identification of latent group structures in spatial dynamic panels. We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model with two-way fixed effects. In addition, we also allow for latent panel structures where individuals can be classified into a few groups such that individuals within the same group share the common slope parameters and do not otherwise. Both the number of groups and the individuals’ group membership are unknown. To identify the latent group structures, we first adopt the GMM to obtain the preliminary unconstrained estimates of the slope coefficients. Then we apply the sequential binary segmentation algorithm (SBSA) of Wang and Su (2021) to these estimates and obtain the clusters. A BIC-type information criterion is proposed to choose the number of latent groups consistently. The asymptotic analysis shows that this method can identify the true group structure consistently, and the post-classification estimators enjoy the oracle property. Monte Carlo simulations demonstrate that our method has good finite sample performance. Finally, we apply our approach to the US housing market and identify two latent Metropolitan Statistical Areas groups.

Keywords: Classification; Heterogeneity; Panel structure model; Spatial dynamic panel data; SBSA algorithm (search for similar items in EconPapers)
JEL-codes: C31 C38 C51 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:1955-1980

DOI: 10.1016/j.jeconom.2023.02.007

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