andy,
if your model is Xt = 0.5 * Xt-1 + e, then it should have
Xt = 0.1 * Xt-1 + 0.4 * Xt-1 + e
(Xt - 0.1*Xt-1) = 0.4 * Xt-1 + e
so what you need to do is to substract part of lag from your series.
it is just my $0.02.
On 2/26/07, Andy Bunn <Andy.Bunn at wwu.edu>
wrote:> I have a time series with a one year lag, ar=0.5. The series has some
> interesting events that disappear when the series is whitened (i.e.,
> fitting an AR process and looking at the residuals). I'd like to remove
> the autocorrelation in stages to see the effect on the time series. Is
> there a way to specify the autocorrelation term while fitting an AR
> process?
>
> For instance, given the following:
>
> x <- arima.sim(model = list(order = c(1,0,0), ar = 0.5), n = 500,
> sd=0.25)
>
> Can I filter x in a way that the autocorrelation at lag one is 0.4, then
> 0.3, 0.2, 0.1, until I get to a clean series equivalent to:
>
> y <- arima(x, order = c(1,0,0))$resid
>
> Thanks in advance,
> Andy
>
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--
WenSui Liu
A lousy statistician who happens to know a little programming
(http://spaces.msn.com/statcompute/blog)