This note derives the bias of the quantile regression estimator in the presence of classical additive measurement error, and show its connection to least squares models. The bias structure suggests that the instrumental variables estimator proposed for least squares can be applied to the quantile regression case.Quantile Regression, Measurement Errors, Instrumental Variables

Gabriel Montes-Rojas

English

Research Papers in Economics

     
 
 
  
  
Volume 31, Issue 4 
  
Quantile Regression with Classical Additive Measurement Errors 
  
 
 
Gabriel Montes-Rojas  
City University London 
Abstract 
This note derives the bias of the quantile regression estimator in the presence of classical additive measurement error, 
and show its connection to least squares models. The bias structure suggests that the instrumental variables estimator 
proposed for least squares can be applied to the quantile regression case.
The author would like to express his appreciation to two anonymous referees, Antonio Galvao, Anil Bera, John Conley, Roger Koenker, 
Arthur Lewbel, Steve Portnoy, participants in the seminars at Cass Business School, University of Alicante, University of Illinois at Urbana-
Champaign, 2008 Midwest Econometrics Group Meeting, 15th Panel Data Conference (Bonn), 2009 Far East and South Asia Meeting of the 
Econometric Society, and 2010 Econometric Society World Congress for helpful comments and discussions. All the remaining errors are mine. 
Citation: Gabriel Montes-Rojas, (2011) ''Quantile Regression with Classical Additive Measurement Errors'', Economics Bulletin, Vol. 31 
No. 4 pp. 2863-2868. 
Contact: Gabriel Montes-Rojas - gabriel.montes-rojas.1@city.ac.uk. 
Submitted: May 24, 2011.   Published: October 11, 2011. 
 
     Economics Bulletin, 2011, Vol. 31 No. 4 pp. 2863-2868
1 Introduction
When the regressors are subject to measurement errors (ME), it is well known that
the slope coecient of the least squares (LS) regression estimator is inconsistent because
the measurement error induces endogeneity in the model. In the one regressor case (or
the multiple regressor case with uncorrelated regressors), under standard assumptions, the
ordinary LS estimator is biased toward zero, a problem often denoted as attenuation. The
most common remedy to reduce this bias caused by the endogeneity problem is to use
either economic theory or intuition to nd additional observable variables that can serve as
instrumental variables (IV). Most of the literature on the estimation of models with ME
is based on LS with IV. See for instance Hsiao and Taylor (1991), Wansbeek and Koning
(1989), Griliches and Hausman (1986) and Wansbeek (2001).
Recently, the topic of ME in variables has also attracted considerable attention in the
quantile regression (QR) literature. Chesher (2001) studies the impact of covariate ME
on quantile functions using small variance approximation, and Schennach (2008) discusses
identication and estimation issues for general quantile functions based on Fourier trans-
forms and previous results for nonlinear models (see Schennach 2004,2008). Wei and Carroll
(2009) proposes a method to correct measurement error induced bias by constructing joint
estimating equations that simultaneously hold for all the quantile levels.
This paper derives the bias in the QR estimator in the presence of a classical additive
ME in covariates using Angrist, Chernozhukov and Fernandez-Val (2006) omitted variables
formula. This representation provides an explicit formulation for the bias in the slope co-
ecients and complements the results in Chesher (2001) and Wei and Carroll (2009). The
QR representation of the ME problem as an omitted variable determines that the corre-
sponding endogeneity bias has a similar structure to that in LS estimation, and therefore it
suggests that LS-based IV strategies in the QR framework as in Chernozhukov and Hansen
(2006,2008) solve the ME problem.
2 Additive measurement error bias in quantile
regression
In this section we show that ME causes endogeneity bias in the QR estimator. Consider
2864Economics Bulletin, 2011, Vol. 31 No. 4 pp. 2863-2868
the following representation of a model with classical additive ME,
yi = x
0
i  + z
0
i + ui i = 1;:::;N; (1)
where yi is the response variable, x
i is a dim(x) = p-vector of the well-measured regressors,
 is a p1 vector of parameters of interest to be estimated, zi is a dim(z)-vector of covariates
without ME and coecients , and ui is the residual. Suppose that we do not observe x
i,
but rather xi, which is a noisy measure of x
i subject to an additive ME i,
xi = x

i + i: (2)
It is assumed that i is independent and identically distributed (iid). Moreover, i is
independent of x
i, zi and ui. Using equation (2) we can express (1) in terms of the observed
y and x as
yi = x
0
i + z
0
i + ui   
0
i: (3)
It follows that the observed regressor xi in (3) will be correlated with the composite error,
ui   i, inducing endogeneity in the model. This problem is of practical signicance since
the resulting bias may be large. The standard result for the LS estimator with ME can be
seen as an omitted variables problem, where  i is the omitted variable.
In the following paragraphs, we derive the bias in the QR estimator in the presence
of ME using Angrist, Chernozhukov and Fernandez-Val (2006) (denoted ACFV hereafter)
approach. As in LS, ME bias in QR can be derived analytically as an endogeneity bias, and
provides a simpler representation than that in Chesher (2001).
Dene v = [x0;z0]0,  = [0;00]0, v = [x0;z0]0 = v +  and ' = [0;0]0 (here we omit
the indexes i to simplify the notation). Let ' be the probability limit of the LS estimator
without ME (i.e. using the well-measured covariate, x), and ' be the probability limit of
the LS estimator with ME (i.e. using x). It is well known that ME produce the following
relation between these two estimators,
'
 = '
   (E [(v
v
0 + 
0
)])
 1 E[
0]: (4)
The bias in the variable with measurement error parameter depends on the noise to signal
ratio, thus generating attenuation bias.
Consider now the th conditional quantile function of the response y,
Qy(jv
) = x
0
() + z
0
(): (5)
2865Economics Bulletin, 2011, Vol. 31 No. 4 pp. 2863-2868
Using equation (2) one can rewrite (5) as
Qy(jv
) = (x   )
0
() + z
0
() = x
0
()   
0
() + z
0
() = Qy(jv;): (6)
As in the standard LS case with ME, the QR counterpart can be seen as an omitted
variable problem, where   is the omitted variable. We derive the approximate bias using
the ACFV omitted variable bias formula. The QR estimator without ME solves
'
() = argmin
'
E[(y   v
0')]; (7)
where (u) := u(   I(u < 0)). However, the QR estimator, as in the LS case, is biased in
the presence of the ME. In this case, in the problem of solving (6) omitting  , the standard
QR solves
'
() = argmin
'
E[(y   v
0')]: (8)
Here '() and '() are the parameters that solve the population minimization problem,
dened in an analog way to ACFV paper.
The following Lemma shows that the ME bias in QR can be approximated to an expres-
sion similar to that in OLS.
Lemma 1 Assume that: (i) the conditional density function fy(yjv;) exists and is bounded
a.s.; (ii) E[y], E[Qy(jv;)2], and Ek[v0;0]0k2 are nite; (iii) '() and '() uniquely solves
equations (7) and (8) respectively; (iv)  is independent of (x;z;u). Then,
'
() = '
()   (E [!(v;)  (vv
0)])
 1 E[!(v;)  v
0
()]: (9)
where !(v;) :=
R 1
0 fu() (u  (v;;'())jv;)du=2 is a weighting function, and (v;;'()) =
v0  ('()   '())0 + 0() is the QR specication error.
Proof. The proof follows ACFV results for partial QR and omitted variables bias (p.545{
548). Since the conditional quantile function is linear, Qy(jv;) = v0'()   0(), where
'() is dened as in (7). Then, the conditional QR model in equation (5) can be seen
as one with [v0;0]0 as covariates. Moreover, the conditional QR with measurement error
Qy(jv) = v'(), obtaining the coecient '() from equation (8), can be seen as a model
with omitted variable .
2866Economics Bulletin, 2011, Vol. 31 No. 4 pp. 2863-2868
Recall that !(v;) :=
R 1
0 fu() (u  (v;;'())jv;)du=2 where fu()(:j:) is the condi-
tional density function of u() := y   Qy(jv;), and (v;;') := v0'   Qy(jv;) is the
bias in the estimated quantile function for a given '. Then,
(v;;'
()) = v
0  ('
()   '
()) + 
0
():
Under the stated assumptions, by Theorem 2 in ACFV, '() uniquely solves the equation
'
() := argmin'E[!(v;)
2
(v;;')]:
Solving for '() we have
'
() = '
()   (E [!(v;)  (vv
0)])
 1 E[!(v;)  v
0
()]:
Note that the weighting function !(:) depends on both v and  and it is a distinctinve
feature of QR when compared with LS case. However, it can be shown that the leading term
in the QR bias has the same form as that in LS. In order to show this, assume that fy(yjv;)
has a rst derivative in y that is bounded in absolute value by  f0(v;) and consider a Taylor
expansion of the weights as in ACFV, p.546,
!(v;) = 1=2  fy(Q(yjv;)jv;) + &(v;);
where
j&(v;)j  1=4  j(v;;'
())j   f
0(v;):
Note that by independence of y and , fy(Q(yjv;)jv;) = fy(Q(yjv)jv) (with rst
derivative bounded by  f0(v)). Then, when either (v;;'()) or  f0(v) is small,
!(v;)  1=2  fy(Q(yjv
)jv
):
Then, the ACFV weighted LS approximation to QR implies that
'
()  '
()   (E [fy(Q(yjv
)jv
)(v
v
0 + 
0
)])
 1 E[fy(Q(yjv
)jv
)
0
()]: (10)
It is important to note that a key factor in the coecient bias approximation given by
(10) is the conditional density function fy(:jv). As in the LS case, the bias in the variable
with measurement error parameter in the QR framework depends on the noise to signal
ratio, thus generating attenuation bias (compare with equation (4)). However, in the QR
case, this bias is weighted by the conditional density function fy(:jv).
2867Economics Bulletin, 2011, Vol. 31 No. 4 pp. 2863-2868
3 Conclusions
This paper frames measurement error problems in quantile regression models. The results
in this paper show that measurement error problems is a subset of endogeneity bias, which
in turn can be solved using instrumental variables techniques.
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2868

Errors in variables in panel data&quot;

Estimation of nonlinear models with measurement error&quot;

GMM estimation in panel data models with measurement error&quot;

Instrumental quantile regression inference for structural and treatment eects models&quot;

Instrumental variable quantile regression: A robust inference approach&quot;

Measurement error and panel data&quot;

Parameter approximations for quantile regressions with measurement error&quot;

Quantile regression under misspeci with an application to the U.S. wage structure&quot;

Quantile Regression with Classical Additive Measurement Errors

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