Bogdan Romocea wrote:
> I want to simulate a time series (stationary; ... <snip> ...
> values is skewed to the right; quite a few ARMA absolute standardized
<snip>
>
> sim <- list(NULL) #simulated
> for (i in 1:5) {
> sim[[i]] <- as.vector(arima.sim(list(ar=c(farma[["coef"]][1]),
> ma=c(farma[["coef"]][2])),n=length(rdtb),innov=rdtb))
> }
>
> I don't understand why the simulation runs generate virtually
> identical values:
<snip>
They are identical because you are using the same
innovations i.e. rdtb, over and over!!!
If you want different results, you have to use
different innovations.
BTW it would seem to make more sense to use the
***residuals*** from your fit to rdtb, rather than rdtb
itself, as your innovations. (But then you would
be essentially reconstructing rdtb.)
You probably want to ***fit*** some distribution to the
residuals from rdtb, and then sample from that distribution
to get your innovations.
cheers,
Rolf Turner
[EMAIL PROTECTED]
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