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Title: Quantile-regression-based clustering for panel data Authors:  Yingying Zhang - Fudan University (China) [presenting]
Huixia Judy Wang - George Washington University (United States)
Zhongyi Zhu - Fudan University (China)
Abstract: In many applications, such as economic and medical studies, it is important to identify subgroups of subjects with different covariate effects. 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 at a single quantile level or at multiple quantiles jointly. Even in cases where the group membership is the same across quantile levels, the signal differentiating subgroups may vary with quantiles. It remains unclear which quantile is preferable or should one use composite regression by combining information across multiple quantiles. To answer this question, we propose a new stability measure to choose among multiple quantiles and the composite quantile that gives the most stable clustering results. The consistency of the proposed parameter and group membership estimation is established. The finite sample performance of the proposed method is assessed through simulation and the analysis of an economy growth data.