Title: Inference for penalized quantile regression for panel data
Authors: Carlos Lamarche - University of Kentucky (United States) [presenting]
Thomas Parker - University of Waterloo (Canada)
Abstract: Penalized quantile regression is a relatively new technique for estimation of panel quantile models. The existing literature has been mostly focused on the consistency of the point estimator under different assumptions. We investigate inference in a class of penalized quantile regression estimators based on wild bootstrap procedures. Simulation studies are carried out to investigate the small sample behavior of the proposed approaches. Finally, we illustrate the application of the new approaches using a real data example.