  This paper introduces estimation methods for grouped latent heterogeneity in
panel data quantile regression. We assume that the observed individuals come
from a heterogeneous population with a 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. Simulations show that the method works
well in finite samples when T is reasonably large. To illustrate the proposed
methodology we study the effects of the adoption of Right-to-Carry concealed
weapon laws on violent crime rates using panel data of 51 U.S. states from 1977
- 2010.

Econometrics;Methodology