Hi,
I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R How to estimate the panel data quantile regression if the regression contains no constant term? I tried to change the code of rq.fit.panel by delect "X=cbind(1,x)" and would like to know is that correct ? Thanks I really would appreciate some suggestions. Best Helen Chen 
This is a nontrivial problem. This comes up often on the Statalist
(qreg is for crosssection quantile regression): " You want to fit a plane through the origin using the L1 norm. This is not as easy as with L2 norm (LS), as it is more than a matter of dropping a constant predictor yet otherwise using the same criterion of fit. You are placing another constraint on a problem that already does not have a closedform solution, and it does not surprise me that qreg does not support this. " (N.J. Cox) http://www.stata.com/statalist/archive/200710/msg00809.html You will probably have to program this by hand. Note also the degeneracy conditions in Koenker (2003, pg. 36). I am not sure how this extends to panel data though. References: @book{koenker2005qre, title={{Quantile Regression; Econometric Society Monographs}}, author={Koenker, R.}, year={2005}, publisher={Cambridge University Press} } T On Sun, Apr 26, 2009 at 8:24 AM, Helen Chen <[hidden email]> wrote: > > Hi, > > I am trying to estimate a quantile regression using panel data. I am trying > to use the model that is described in Dr. Koenker's article. So I use the > code the that is posted in the following link: > > http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R > > How to estimate the panel data quantile regression if the regression > contains no constant term? I tried to change the code of rq.fit.panel by > delect "X=cbind(1,x)" and would like to know is that correct ? > > > Thanks > I really would appreciate some suggestions. > Best > Helen Chen >  > View this message in context: http://www.nabble.com/Questionof%22QuantileRegressionforLongitudinalData%22tp23239896p23239896.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/rhelp > PLEASE do read the posting guide http://www.Rproject.org/postingguide.html > and provide commented, minimal, selfcontained, reproducible code. >  To every ωconsistent recursive class κ of formulae there correspond recursive class signs r, such that neither v Gen r nor Neg(v Gen r) belongs to Flg(κ) (where v is the free variable of r). ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/rhelp PLEASE do read the posting guide http://www.Rproject.org/postingguide.html and provide commented, minimal, selfcontained, reproducible code. 
I was trying to resist responding to this question since the original
questioner had already been admonished twice last october about asking questions on Rhelp about posted code that was not only not a part of Rbase, but not even a part of an R package. But the quoted comment about Stata is too enticing a provocation to resist. First, it should be said that omitting intercepts in any regression setting should be undertaken "at one's peril" it is generally a very dangerous activity, somewhat akin to fitting interactions without main effects, but if there is a good rational for it, it is no different in principle for median regression than for mean regression. It may well be that Stata prohibits this sort of thing out of some sort of paternalistic motive, but in R the usual formula convention y ~ x1 + x2 1 suffices. Of course it situations in which such a formula is used for several quantiles it should be understood that it is forcing each conditional quantile function through the origin effectively implies that the conditional distribution degenerates to a point mass at the origin. Second, I would like to remark that "closedform solutions" are in the eye of the beholder, and many people who can recall the infamous formula: betahat = (X'X)^{1} X'y would be hard pressed to dredge up enough linear algebra to use the formula for anything more than the bivariate case on the proverbial desert island without the aid of their trusty laptop "Friday". Finally, cbind(1,x) does introduce an intercept in the code originally asked about, so if you don't want an intercept don't do that, but be sure that that is really want you want to do. url: www.econ.uiuc.edu/~roger Roger Koenker email [hidden email] Department of Economics vox: 2173334558 University of Illinois fax: 2172446678 Champaign, IL 61820 On Apr 26, 2009, at 6:35 AM, Tirthankar Chakravarty wrote: > This is a nontrivial problem. This comes up often on the Statalist > (qreg is for crosssection quantile regression): > " > You want to fit a plane through the origin using the L1 norm. > This is not as easy as with L2 norm (LS), as it is more > than a matter of dropping a constant predictor yet otherwise using the > same criterion of fit. You are placing another constraint on a > problem that already does not have a closedform solution, > and it does not surprise me that qreg does not support this. > " (N.J. Cox) > http://www.stata.com/statalist/archive/200710/msg00809.html > > You will probably have to program this by hand. Note also the > degeneracy conditions in Koenker (2003, pg. 36). I am not sure how > this extends to panel data though. > > References: > @book{koenker2005qre, > title={{Quantile Regression; Econometric Society Monographs}}, > author={Koenker, R.}, > year={2005}, > publisher={Cambridge University Press} > } > > T > > On Sun, Apr 26, 2009 at 8:24 AM, Helen Chen <[hidden email]> > wrote: >> >> Hi, >> >> I am trying to estimate a quantile regression using panel data. I >> am trying >> to use the model that is described in Dr. Koenker's article. So I >> use the >> code the that is posted in the following link: >> >> http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R >> >> How to estimate the panel data quantile regression if the regression >> contains no constant term? I tried to change the code of >> rq.fit.panel by >> delect "X=cbind(1,x)" and would like to know is that correct ? >> >> >> Thanks >> I really would appreciate some suggestions. >> Best >> Helen Chen >>  >> View this message in context: http://www.nabble.com/Questionof%22QuantileRegressionforLongitudinalData%22tp23239896p23239896.html >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> [hidden email] mailing list >> https://stat.ethz.ch/mailman/listinfo/rhelp >> PLEASE do read the posting guide http://www.Rproject.org/postingguide.html >> and provide commented, minimal, selfcontained, reproducible code. >> > > > >  > To every ωconsistent recursive class κ of formulae there correspond > recursive class signs r, such that neither v Gen r nor Neg(v Gen r) > belongs to Flg(κ) (where v is the free variable of r). > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/rhelp > PLEASE do read the posting guide http://www.Rproject.org/postingguide.html > and provide commented, minimal, selfcontained, reproducible code. ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/rhelp PLEASE do read the posting guide http://www.Rproject.org/postingguide.html and provide commented, minimal, selfcontained, reproducible code. 
I've used the function rq.fit.sfn and rq.fit.panel to estimate a quantile regression on a panel data set.Now I would like to compute an statistic to measure the goodness of fit of this model (Tstatistics and Pvalue ).
Does someone know how could I do that? best wish 
<quote author="ywh123">
I've used the function rq.fit.sfn and rq.fit.panel to estimate a quantile regression on a panel data set.Now I would like to compute an statistic to measure the goodness of fit of this model (Tstatistics and Pvalue ). Does someone know how could I do that? For formal inference you are better off using rqss() in the quantreg package, but beware that formal inference for shrinkage estimators is still an active research topic. For goodness of fit statistics like the usual regression R^2, see FAQ() item 4 in the quantreg package. url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker@uiuc.edu Department of Economics vox: 2173334558 University of Illinois fax: 2172446678 Urbana, IL 61801 
Thanks for your help
RKoenker I want to deal with the problem through bootstrap.so I can get pvalue and Tstatistics. Do you think so? 
How to interprete the results of panel data models of R? I estimate a adapted form of Koenker's (2004) suggestion for a quantile regression approach with panel data, for my data:
rq.fit.panel < function(X,Y,s,w,taus,lambda) { require(SparseM) require(quantreg) K < length(w) if(K != length(taus)) stop("length of w and taus must match") X < as.matrix(X) p < ncol(X) n < length(levels(as.factor(s))) N < length(y) if(N != length(s)  N != nrow(X)) stop("dimensions of y,X,s must match") Z < as.matrix.csr(model.matrix(~as.factor(s)1)) Fidelity < cbind(as(w,"matrix.diag.csr") %x% X,w %x% Z) Penalty < cbind(as.matrix.csr(0,n,K*p),lambda*as(n,"matrix.diag.csr")) D < rbind(Fidelity,Penalty) y < c(w %x% y,rep(0,n)) a < c((w*(1taus)) %x% (t(X)%*%rep(1,N)), sum(w*(1taus)) * (t(Z) %*% rep(1,N)) + lambda * rep(1,n)) rq.fit.sfn(D,y,rhs=a) } But i don't have the estimate significances, i don't know identify the results below: $coef [1] 1.02281339 0.18750668 0.13688807 0.04180458 0.01367417 1.02872440 0.18055062 0.13003224 0.03829135 0.01409369 1.03377335 0.16649845 0.11669812 [14] 0.03854060 0.01438620 1.03851101 0.15328087 0.10440359 0.03871744 0.01465492 1.04330584 0.14660960 0.09670756 0.03465501 0.01430647 0.29187982 [27] 0.21831160 0.11295134 0.21530494 0.15664777 0.13840296 0.03224749 0.11692122 0.11237144 0.15112171 0.10385352 0.08385934 0.16090525 0.30349309 [40] 0.16121494 0.03106264 0.16299994 0.03182579 0.22271685 0.08251486 0.29031224 0.19680023 0.20004209 0.05601186 0.21140762 0.04254752 0.01864703 $ierr [1] 0 $it [1] 16 $time [1] 0 ##summary rq summary(rq) Length Class Mode coef 52 none numeric ierr 1 none numeric it 1 none numeric time 1 none numeric Anybody help me? 
Quantile Regression for Longitudinal Data

How to solve the panel data of cqr by writing the R code or using the "quantreg" ?
Thank you! 
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