Quantile-regression-based clustering for panel data

Yingying Zhang, Huixia Judy Wang and Zhongyi Zhu

Journal of Econometrics, 2019, vol. 213, issue 1, 54-67

Abstract: In panel data analysis, it is important to identify subgroups of units with heterogeneous parameters. This can not only increase the model flexibility but also produce more efficient estimation by pooling information across units within the same group. In this paper, we propose a new quantile-regression-based clustering method for panel data. We develop an iterative algorithm using a similar idea of k-means clustering to identify subgroups with heterogeneous slopes at a single quantile level or across multiple quantiles. The asymptotic properties of the group membership estimator and corresponding group-specific slope estimator are established. The finite sample performance of the proposed method is assessed through simulation and the analysis of an economic growth data.

Keywords: Fixed effects; Heterogeneity; Panel data; Quantile regression; Subgroup identification (search for similar items in EconPapers)
JEL-codes: C13 C21 C23 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:213:y:2019:i:1:p:54-67

DOI: 10.1016/j.jeconom.2019.04.005

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