[R] spurious regression in R
Spencer Graves
spencer.graves at pdf.com
Fri Jul 8 04:16:35 CEST 2005
R has extensive time series capabilities within base R and especially
add-on packages like the "dse" bundle and the several packages
associated with "www.rmetrics.org". I'm still a novice in this area.
The source I've found most useful so far is the time series chapter in
Venables and Ripley (2002) Modern Applied Statistics with S, 4th ed.
(Springer).
Also, are you familiar with "vignette()"? I found interesting the
vignettes for "zoo", "dse1", and "dse2", accessed for example as follows:
z <- vignette("zoo")
z # This brings up a tutorial in Adobe Acrobat
edit(z)
# This extracts the R commands from the tutorial
# into a separate file that can be processed line by line,
# edited to test alternatives, etc.
# NOTE: "edit(z)" did NOT work for me under XEmacs.
# If you use XEmacs, try "Stangle(z$file)";
# this should create a *.R file in "getwd()"
I suspect this will not answer your questions, but I hope it helps.
Feel free to submit another question. However, before you do, I suggest
you read the posting guide!
"http://www.R-project.org/posting-guide.html". It might help you answer
many of your questions yourself and increase the utility of answers you
receive to other questions you post to this list.
spencer graves
yyan liu wrote:
> Hi:
> I am trying to do a spurious regression in R but I
> can not find the function. Anybody used it before? The
> problem I have is try to do a regression with several
> time series. An alternative is to use the GLS function
> to fit the linear regression with the correlation
> structure AR(3) for the response (or residual). I hope
> the residuals after the GLS regression will be
> independent judged by Box-Ljung test. However, I dont
> know how the residuals are defined in the GLS
> function. Is it just y-yhat or y-yhat times the
> (covariance matrix)^(-1/2). Because y-yhat still has
> the AR(3) covariance structure and surely be rejected
> by the Box-Ljung test. The latter will be independent
> if the assumption of AR(3) correlation structure is
> right. Any suggestion are highly appreciated.
> Thx!
>
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--
Spencer Graves, PhD
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