Thats nice thanks =) .. I can trick R to do multivariate armax with
lagged inputs as well and I bet R people didnt designed it that way
(but the idea is the same when doing MLE, it must work)..
anyway.. I wrote a small code (you can change it if you want) that
does armax with multiple inputs in ma
You can have lagged inputs in the xreg statement, you just have to construct
the input matrix properly so the dimensions are the same, e.g.,
x = ts.intersect(mort, trend, part, lag(part,-4))
arima(x[,1],order=c(2,0,1), xreg=x[,2:4])
... and yes you have to worry about singularities or even multi
On Sep 11, 6:24 am, David Stoffer <[EMAIL PROTECTED]> wrote:
> Your model is close, but not correct... there are no t's on the parameters
> and the U's aren't lagged.
>
> You can find an ARMAX example on our "quick fix"
> page:http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm. T
Your model is close, but not correct... there are no t's on the parameters
and the U's aren't lagged.
You can find an ARMAX example on our "quick fix" page:
http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm . The
example is near the bottom and just above the spectral analysis e
Dear R-help-archive..
I am trying to figure out how to make arima prediction when I have a
process involving multivariate time series input, and one output time
series (output is to be predicted) .. (thus strictly speaking its an
ARMAX process). I know that the arima function of R was not designe
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