Essays on Quantile Regression for Dynamic Panel Data Models

The third chapter develops penalized quantile regression methods for dynamic panel data with fixed effects. We consider a penalized strategy designed to improve the properties of the dynamic panel data quantile regression instrumental variables estimator. The penalty involves l1 shrinkage of the fixed effects. We discuss a tuning parameter selector based on the Schwartz information criterion, and propose a bootstrap resampling procedure for constructing confidence intervals for the parameters of interest. Monte Carlo simulations illustrate the dramatic improvement in the performance of the proposed estimator compared with the fixed effects quantile regression instrumental variables estimator. Finally, we provide an application to the partial adjustment toward target capital structures. The results show evidence that there is substantial heterogeneity in the speed of adjustment among firms.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Economics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Galvao, Antonio Fialho, Jr

Contributors dc:contributor

• Koenker, Roger W.

• Economics, Theory

Language dc:language

eng

Identifier

(MiAaPQ)AAI3392019

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/85598