Kit Baum replied to Teofilo Ozuna:
> I would just suggest starting from first principles, as one would in
> performing a Monte Carlo experiment, and writing the data generating
> process. E.g. for an AR(2) model, one can just do
>
> clear
> set obs 1000
> g t = _n
> tsset t
> * AR(2)
> g eps1 = invnorm(uniform())
> scalar theta0= 100
> scalar theta1= 0.6
> scalar theta2= -0.3
> g double ar2 = 0
> qui replace ar2 in 3/l = theta0 + theta1*L.ar2 + theta2*L2.ar2 + eps1
> su
> * should reproduce the parameters of the model (arima not needed for
> pure AR)
> reg ar2 L(1/2).ar2
In a reply I posted to Chris Carambas a couple of weeks ago, I mentioned
the GLS-ARMA procedure described by Stimson (1985). I couldn't find
anything on it using -findit- or -net search-. Is this handled
automatically in -arima-, or can the above code be modified to achieve
this?
CLIVE NICHOLAS |t: 0(044)191 222 5969
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
Stimson, JA (1985) "Regression in Space and Time: A Statistical Essay"
AMER J POL SCI 29(4): 914-47
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