----- Original Message -----
From: mehryar_karim <[email protected]>
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 ([email protected]) 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).
You might also find useful Roger Koenker's paper "Quantile Regression for Longitudinal Data" ( http://www.econ.uiuc.edu/~roger/research/panel/long.pdf )
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."
Hope this helps,
Scott
--------------------------------------------------------------------------------------
. set seed 123
. sqreg price mpg fore, q(.1 .9)
(fitting base model)
(bootstrapping ....................)
Simultaneous quantile regression Number of obs = 74
bootstrap(20) SEs .10 Pseudo R2 = 0.0878
.90 Pseudo R2 = 0.2546
------------------------------------------------------------------------------
| Bootstrap
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q10 |
mpg | -71.42857 46.74642 -1.53 0.131 -164.6383 21.78114
foreign | 591.8571 508.9618 1.16 0.249 -422.9839 1606.698
_cons | 5370.429 979.445 5.48 0.000 3417.471 7323.386
-------------+----------------------------------------------------------------
q90 |
mpg | -348 82.17086 -4.24 0.000 -511.844 -184.156
foreign | 1654 961.6789 1.72 0.090 -263.5332 3571.533
_cons | 16257 1973.42 8.24 0.000 12322.11 20191.89
------------------------------------------------------------------------------
. set seed 123
. sqreg price mpg fore, q(.1 .9)
(fitting base model)
(bootstrapping ....................)
Simultaneous quantile regression Number of obs = 74
bootstrap(20) SEs .10 Pseudo R2 = 0.0878
.90 Pseudo R2 = 0.2546
------------------------------------------------------------------------------
| Bootstrap
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q10 |
mpg | -71.42857 46.74642 -1.53 0.131 -164.6383 21.78114
foreign | 591.8571 508.9618 1.16 0.249 -422.9839 1606.698
_cons | 5370.429 979.445 5.48 0.000 3417.471 7323.386
-------------+----------------------------------------------------------------
q90 |
mpg | -348 82.17086 -4.24 0.000 -511.844 -184.156
foreign | 1654 961.6789 1.72 0.090 -263.5332 3571.533
_cons | 16257 1973.42 8.24 0.000 12322.11 20191.89
------------------------------------------------------------------------------
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