Quantile regression and the duration of unemployment

Powell (1986) proposed a quantile regression estimator for censored regression models on the basis of equivariance of quantiles to monotone transformations. In this thesis, censored quantile regression models are generalized using two-parameter Box-Cox transformation to relax the conventional linear specification of functional form, and the quantile regression estimator of the parameters of the transformed and censored regression models is presented. Both the $N\sp{1/2}$-consistency and the asymptotic normality of quantile estimator are derived for nonlinear regression models. The proof of asymptotic normality is based on the approach introduced by Pollard (1989) using maximal inequalities and quadratic approximation to the objective function, thus simplifying the argument and relaxing the need for convexity of the objective function in the parameter vector.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Economics, General

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Park, Beum-Jo

Contributors dc:contributor

• Koenker, Roger W.

• Economics, General
• Economics, Labor

Statement dc:rights

• Copyright 1992 Park, Beum-Jo

Language dc:language

eng

Identifier

AAI9305647
(UMI)AAI9305647

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/23657