How is it possible to estimate the conditional autoregressive Value-at-Risk
model
qantile_t(tau)=a0+a1*qantile_(t-1)(tau)+a2*abs(r_(t-1))
see http://www.faculty.ucr.edu/~taelee/paper/BLSpaper1.pdf (page 10)) of Engle
& Manganelli in R? The qantile_(t-1)(tau)-term causes headache.
Kind regard
Hi,
how to derive an estimate of skewness and kurtosis out of a predicted
distribution by quantile regression?
Example:
library(quantreg)
data(airquality)
airq <- airquality[143,]
f <- rq(Ozone ~ ., data=airquality,tau=seq(0.01,0.99,0.01))
predict(f,newdata=airq)
Any suggestions?
Kind regards,
Hi Martin,
Efferz, Martin finance.uni-mainz.de> writes:
>
> Hi,
>
> how to measure the goodness of fit, when using the rq() function of quantreg?
I need something like an R^2 for
> quantile regression, a single number which tells me if the fit of the whole
quantile process (not only for a
> si
Hi,
how to measure the goodness of fit, when using the rq() function of quantreg? I
need something like an R^2 for quantile regression, a single number which tells
me if the fit of the whole quantile process (not only for a single quantile) is
o.k. or not.
Is it possible to compare the (condi
Brian,
It is hard to say at this level of resolution of the question, but it
would seem that you might
be able to start by considering each sample vector as as repeated
measurement of the
fiber length -- so 12 obs in the first 1/16th bin, 235 in the next
and so forth, all associated
with som
I am relatively new to R, but am intrigued by its flexibility. I am interested
in quantile regression and quantile estimation as regards to cotton fiber
length distributions. The length distribution affects spinning and weaving
properties, so it is desirable to select for certain distribution
> data(engel)
> attach(engel)
> rq(y~x)
Call:
rq(formula = y ~ x)
Coefficients:
(Intercept) x
81.4822474 0.5601806
Degrees of freedom: 235 total; 233 residual
> rq(y~x)->f
> f$tau
[1] 0.5
url:www.econ.uiuc.edu/~rogerRoger Koenker
email[EMAIL PROTECTED]
Hi,
how is it possible to retrieve the corresponding tau value for each observed
data pair (x(t) y(t), t=1,...,n) when doing a quantile regression like
rq.fit <- rq(y~x,tau=-1).
Thank you for your help.
Jaci
--
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R-help@stat.math.ethz.ch mailing li
Hi,
I load my data set and separate it as folowing:
presu <- read.table("C:/_Ricardo/Paty/qtdata_f.txt", header=TRUE, sep="\t",
na.strings="NA", dec=".", strip.white=TRUE)
dep<-presu[,3];
exo<-presu[,4:92];
Now, I want to use it using the wls and quantreg packages. How I change the
data classes
On 10-Dec-05 [EMAIL PROTECTED] wrote:
> Dear List members,
>
> I would like to ask for advise on quantile regression in R.
>
> I am trying to perform an analysis of a relationship between
> species abundance and its habitat requirements -
> the habitat requirements are, however, codes - 0,1,2,3..
Since almost all (95%) of the observations are concentrated at x=0
and x=1,
any fitting you do is strongly influenced by what would be obtained
by simply fitting quantiles at these two points and interpolating, and
extrapolating according to your favored model. I did the following:
require(quan
Dear List members,
I would like to ask for advise on quantile regression in R.
I am trying to perform an analysis of a relationship between species abundance
and its habitat requirements -
the habitat requirements are, however, codes - 0,1,2,3... where 0<1<2<3
and the scale is linear - so I wo
Dear Brian,
thanks for your mail. For other reasons I need a local
polynomial. The nonparametric regression code is very
scetchy, but I have used it as base anyway.
Best
Stefan
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I have the following problem: I would like to do a
nonparametric quatile regression. Thus far I have used
the quantreg package and done a local quadratic, but
it does not seem to work well.
Alternatively, I have tried with an older S version I
have the function rreg, and used
rreg(datax,datay,me
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.
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
OK, Thank you all very much for the help!
Best regards
Chris.
Christoph Scherber wrote:
Dear colleagues,
How can I do quantile regression with R?
Best regards
Chris.
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Christoph Scherber <[EMAIL PROTECTED]> writes:
> Dear colleagues,
>
> How can I do quantile regression with R?
Package quantreg springs to mind...
--
O__ Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenh
Please read the footer of the message, and follow the link. Besides, you
don't need people googling for you, do you?
Andy
> From: Christoph Scherber
>
> Dear colleagues,
>
> How can I do quantile regression with R?
>
> Best regards
> Chris.
>
> __
Dear colleagues,
How can I do quantile regression with R?
Best regards
Chris.
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PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
I'd like to mention that there is a new quantile regression package
"nprq" on CRAN for additive nonparametric quantile regression estimation.
Models are structured similarly to the gss package of Gu and the mgcv
package of Wood. Formulae like
y ~ qss(z1) + qss(z2) + X
are interpreted a
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