"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. ###################################################################### . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
