Re: st: Is there a fixed effect quantile regression in STATA?
Date
Fri, 30 Jul 2004 10:21:27 -0700
Thank you, Scott. I will check out the references you suggested me. Before
I do so, I would like to expand my orignial question a bit.
I have a cross-sectional data on children and their familes. I want to
"control for" the family unobservable (the family fixed effect). I have used
xtreg y x, fe (family_id)
Now I am interested in the effect of x on a particular percentile
(quantile) of y. I would like to "differ out" the family unobservable in
_qreg_ or _sqreg_.
Your suggestion about creating dummies may not work very well in my case,
because (1) I have a large number of families, and each family has a small
(and unbalanced) number of children, say from 1 to 6; and (2) I do not need
to estimate the coefficients of the fixed effect, all I want is to control
for. I am looking for something (a STATA command) to do what _areg_ does
for the mean response estimates.
Thank you for your help,
Bo
===============
Date: Thu, 29 Jul 2004 13:45:43 -0500
From: smerryman@kc.rr.com
Subject: Re: st: Is there a fixed effect quantile regression in STATA?
- ----- Original Message -----
From: mehryar_karim <akarim@tulane.edu>
Date: Thursday, July 29, 2004 11:49 am
Subject: Re: st: Is there a fixed effect quantile regression in STATA?
> I achieved the fixed effect results using dummy variables for
> subjects in the sqreg model. My major issue was the reproducibility
> of the standard errors even after using the set seed command before
> implementing `sqreg'. I think there is a bug in the `sqreg' command.
> I'm not sure if my response was helpful.
Could you please expand on the issue of the non-reproducibility of the
standard errors? It seems to work for me (results below)
As to Bo's (bo@macinnis.org) original question on fixed effects quantile
regression -- you would have to generate dummy variables and include them
in the regression. I believe this would be interpreted as a pure-location
shift. This seems to how it is done in the applied literature (see, for
example, "A Quantile Regression Analysis of the Cross Section of Stock
Market Returns" by Michelle L. Barnesa1 and Anthony W. Hughesb
(http://www.bos.frb.org/economic/wp/wp2002/wp022.pdf) who use time dummies
to control time specific effects).
In it he writes (page 3):
"What role should the a_i's play? Generally, the a_i's would be intended to
capture some individual specific source of variability, or 'unobserved
heterogeneity,'
that was not adequately controlled for by other covariates in the model.
For example,
in a study of the effect of a dietary intervention on blood pressure, it
would be
desirable to estimate departures from individuals' idiosyncratic levels. If
the number
of observations m_i were large for each individual then we might even hope
to estimate
a distributional shift a_i(t) for each individual. This would certainly be
useful for
groups of individuals: a distributional shift for men versus women, or for
blacks
versus whites. However, in most applications the m_i, the number of
observations on
each individual, would be relatively modest and then it is quite
unrealistic to attempt
to estimate a t-dependent, distributional, individual effect. At best we
may be able to
estimate an individual specific location-shift effect, and even this may
strain credulity."
