** Quantile Regression for Longitudinal Data
**
Roger Koenker

The penalized least squares interpretation of
the classical random effects estimator suggests a possible way forward
for quantile regression models with a large number of fixed effects.
Sparse linear algebra and interior point methods for solving large linear
programs are essential practical tools.

This paper appears in 2004 in J. of Mult. Analysis, 91, 74-89.

A very basic version of the software is available
here.
Note added October 26 2010: It is now possible to do what is done with this
code much more easily using the package rqpd available from R-forge:
here.
In the context of the usual quantreg package some functionality is provided by the
function rqss(). The fixed effect parameters can be introduced in the usual
R way via a factor variable, and the shrinkage, if desired, can be imposed with
the lasso penalty method. See ?rqss for further details.
The paper is available in pdf.

Comments are, of course, always welcome.