KAUST Grant NumberOSR-2015-CRG4-2582
Embargo End Date2020-09-26
Permanent link to this recordhttp://hdl.handle.net/10754/668605
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AbstractIn 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.
CitationZhang, Y., Wang, H. J., & Zhu, Z. (2019). Quantile-regression-based clustering for panel data. Journal of Econometrics, 213(1), 54–67. doi:10.1016/j.jeconom.2019.04.005
SponsorsThe authors would like to thank two anonymous reviewers and the editor for constructive comments and helpful suggestions. This research is supported by National Science Foundation grant DMS-1712760, the OSR-2015-CRG4-2582 grant from KAUST, the National Natural Science Foundation of China grants 11671096, 11690013 and 11731011, a fellowship from China Scholarship Council, the Key Laboratory for Applied Statistics of MOE, Northeast Normal University130028849, and the IR/D program from the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
JournalJournal of Econometrics