Panel Data Quantile Regression with Grouped Fixed Effects
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This paper introduces grouped latent heterogeneity in panel data quantile regression. More precisely, we assume that the observed individuals come from a heterogeneous population with an unknown, finite number of types. The number of types and group membership is not assumed to be known in advance and is estimated by means of a convex optimization problem. We provide conditions under which group membership is estimated consistently and establish asymptotic normality of the resulting estimators.
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