Hello,
Sorry -;0))) I don't know SEM.

Understand and agree...


With best regards,
Oskar


U�ytkownik nothanks <[EMAIL PROTECTED]> w wiadomooci do grup dyskusyjnych
napisa�:[EMAIL PROTECTED]
> "Lone Wolf" <[EMAIL PROTECTED]> wrote in message
news:<afaeu0$dkk$[EMAIL PROTECTED]>...
> > Hello,
> >
> > U�ytkownik nothanks <[EMAIL PROTECTED]> w wiadomooci do grup
dyskusyjnych
> > napisa�:[EMAIL PROTECTED]
> > > Hello sci.stat* readers,
>
> >>>>> Original Snipped <<<<<
>
> > >
> > >
> > Ad. to campare pred. ver. outcom.:
> > Don't want "to hurt" you, but nothing new to make "perfect" forecasting
> > model. Could you tell:
> > 1. In which period (ex ante or ex post veryfied in time) you model gives
> > perfect forecasts? If it's ex ante (cut datas) be worry...
> > 2. Could you tell something more about stability?
> > En example: take regular harmonic regression (with n harmonic
components)
> > and check my sugestions - time to time better way is to take model with
> > worse fit, but with ability to produce stability and probabilty of real
> > forecasts.
> > If you don't want to do this, take any simulation method (ex. neural
> > network). In this case you will get PERFECT forecasts.
> > Please, don't offend. I'm very interesting in you model...
> >
> > With best regards,
> > Oskar Czechowski
>
> Hi Oskar,
>
>   I think you misunderstood my post.   The perfect fit I'm referring to
> is of the sample covariance matrix.   Not the observed outcomes.
> So, in SEM, you can set up the parameters in your covariance matrix
> so that the parameter estimates can be perfectly fit to your sample
> covariance matrix.
>
>   However, in using the subsequent estimated effect coefficients, the
> difference between the observed outcomes and the predicted could
> be very large.  So you would in fact have terrible forecasts.
> My goal isn't to create perfect forecasts, only a "good" model with
> "good" predictions.
>
>
>
> ######################################################################


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