R help - Jun 2004 - Quantile Regression in R

I recently learn about Quantile Regression in R.
I am trying to study two time series (attached) by Quantile Regression in R.
I wrote the following code and do not know how to interpret the lines.
   
What kind of information can I get from them? Correlation for quantiles, 
conditional probabilties (i.e. P(X in Quantile i | Y in Quantile i)) , and etc.
Many thanks in advance for any help.

Best,
Ali

library("quantreg")
#help.start()

Data <- read.table("RESvsMOVE2.dat")
#
x <- Data[,2]
y <- Data[,1]

par(mfrow=c(2,2))

qqnorm(x,main="MOVE Norm Q-Q Plot", xlab="Normal
Qunatiles",ylab = "MOVE Quantiles")
qqline(x)

qqnorm(y,main="Residuals Norm Q-Q Plot", xlab="Normal
Qunatiles",ylab = "Residuals Quantiles")
qqline(y)

plot(x,y,xlab="MOVE",ylab="Residuals",cex=.5)

xx <- seq(min(x),max(x),.5)

# Just a linear regression
g <- coef(lm(y~x))
yy <- (g[1]+g[2]*(xx))
lines(xx,yy,col="yellow")

taus <- c(.05,.1,.25,.5,.75,.9,.95)

for(tau in taus){
        f <- coef(rq(y~x,tau=tau,method="pfn"))
        yy <- (f[1]+f[2]*(xx))
        if (tau ==.05){
             lines(xx,yy,col="red")
        }
        if (tau ==.95){
             lines(xx,yy,col="green")
        }
        if (tau != .05 & tau != .95){
             lines(xx,yy,col="blue")
        }
}



		
__________________________________

The short answer to your question is that  quantile regression
estimates are estimating linear conditional quantile functions,
just like lm() is used to estimate conditional mean functions.

A longer answer would inevitably involve unpleasant suggestions
that you should follow the posting guide:

	a.)  send questions about packages to the maintainer, not R-help
	b.)  not attach datasets in modes that are stripped by R-help
	c.)  make a token effort to read the documentation and related 
literature



url:	www.econ.uiuc.edu/~roger        	Roger Koenker
email	rkoenker at uiuc.edu			Department of Economics
vox: 	217-333-4558				University of Illinois
fax:   	217-244-6678				Champaign, IL 61820

On Jun 29, 2004, at 10:26 AM, Ali Hirsa wrote:
> I recently learn about Quantile Regression in R.
> I am trying to study two time series (attached) by Quantile Regression 
> in R.
> I wrote the following code and do not know how to interpret the lines.
>
> What kind of information can I get from them? Correlation for 
> quantiles,
> conditional probabilties (i.e. P(X in Quantile i | Y in Quantile i)) , 
> and etc.
> Many thanks in advance for any help.
>
> Best,
> Ali
>
> library("quantreg")
> #help.start()
>
> Data <- read.table("RESvsMOVE2.dat")
> #
> x <- Data[,2]
> y <- Data[,1]
>
> par(mfrow=c(2,2))
>
> qqnorm(x,main="MOVE Norm Q-Q Plot", xlab="Normal
Qunatiles",ylab =
> "MOVE Quantiles")
> qqline(x)
>
> qqnorm(y,main="Residuals Norm Q-Q Plot", xlab="Normal
Qunatiles",ylab
> = "Residuals Quantiles")
> qqline(y)
>
> plot(x,y,xlab="MOVE",ylab="Residuals",cex=.5)
>
> xx <- seq(min(x),max(x),.5)
>
> # Just a linear regression
> g <- coef(lm(y~x))
> yy <- (g[1]+g[2]*(xx))
> lines(xx,yy,col="yellow")
>
> taus <- c(.05,.1,.25,.5,.75,.9,.95)
>
> for(tau in taus){
>         f <- coef(rq(y~x,tau=tau,method="pfn"))
>         yy <- (f[1]+f[2]*(xx))
>         if (tau ==.05){
>              lines(xx,yy,col="red")
>         }
>         if (tau ==.95){
>              lines(xx,yy,col="green")
>         }
>         if (tau != .05 & tau != .95){
>              lines(xx,yy,col="blue")
>         }
> }
>
>
>
> 		
> __________________________________
>
>
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> PLEASE do read the posting guide! 
> http://www.R-project.org/posting-guide.html