Regression clustering for panel-data models with fixed effects
Abstract. In this article, we describe the xtregcluster command, which implements
the panel regression clustering approach developed by Sarafidis and Weber
(2015, Oxford Bulletin of Economics and Statistics 77: 274–296).
The method classifies individuals into clusters, so that within each cluster,
the slope parameters are homogeneous and all intracluster heterogeneity is due
to the standard two-way error-components structure. Because the clusters are
heterogeneous, they do not share common parameters. The number of clusters and
the optimal partition are determined by the clustering solution, which
minimizes the total residual sum of squares of the model subject to a penalty
function that strictly increases in the number of clusters. The method is
available for linear short panel-data models and useful for exploring
heterogeneity in the slope parameters when there is no a priori knowledge about
parameter structures. It is also useful for empirically evaluating whether any
normative classifications are justifiable from a statistical point of view.
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Demetris Christodoulou, Vasilis Sarafidis
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xtregcluster, panel data, parameter heterogeneity
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