Mahalanobis Metric Based Clustering for Fixed Effects Model
Chihwa Kao (),
Min Seong Kim () and
Zhonghui Zhang ()
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Chihwa Kao: University of Connecticut
Min Seong Kim: University of Connecticut
Zhonghui Zhang: University of Connecticut
Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 2, No 8, 493-506
Abstract In this paper, we propose a Mahalanobis metric based k-means algorithm (KMM) for group membership estimation in linear panel data models with time-varying grouped fixed-effects by Bonhomme and Manresa (Econometrica 83, 1147–1184, 2015). The proposed method improves the accuracy of estimates by taking serial correlation and heteroscedasticity into account. We also derive the optimal β for group membership estimation and show that it may be different from the true coefficient parameter. Since the optimal β is not feasible in practice, we propose the data driven selection method for its implementation.
Keywords: Mahalanobis metric; k-means; time-varying group fixed effects.; Primary C13; C23; C38; C63; Secondary 62J12 (search for similar items in EconPapers)
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