From: Gabor Grothendieck <ggrothendieck_at_gmail.com>

Date: Sat 09 Jul 2005 - 09:47:53 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sat Jul 09 09:53:46 2005

Date: Sat 09 Jul 2005 - 09:47:53 EST

On 7/8/05, yyan liu <zhliur@yahoo.com> wrote:

*> Hi:
*

> I have two time series y(t) and x(t). I want to

*> regress Y on X. Because Y is a time series and may
**> have autocorrelation such as AR(p), so it is not
**> efficient to use OLS directly. The model I am trying
**> to fit is like
**> Y(t)=beta0+beta1*X(t)+rho*Y(t-1)+e(t)
**>
**> e(t) is iid normal random error. Anybody know whether
**> there is a function in R can fit such models? The
**> function can also let me specify how many beta's and
**> rho's I can have in the model.
**>
*

Make sure you have the latest version of packages dyn and zoo from CRAN and try this:

library(dyn) # this also pulls in zoo

set.seed(1)

x <- zoo(rnorm(50))

y <- 1 + x + rnorm(49)

# regress y on x

y.lm.1 <- dyn$lm(y ~ x)

y.lm.1

# regress y on x and lag of y. We use update to just add last term to model. y.lm.2 <- update(y.lm.1, . ~ . + lag(y, -1)) y.lm.2

anova(y.lm.1, y.lm.2) # difference not significant. Use y.lm.1.

# regress y on x and its lag and on lags 1 and 2 of y # this shows that in lag(x,k) that k may be a vector y.lm.3 <- dyn$lm(y ~ x + lag(x,-1) + lag(y, -seq(2))) y.lm.3

For more info try:

package?dyn

vignette("zoo")

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sat Jul 09 09:53:46 2005

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