[EMAIL PROTECTED] (Pradyumna S Upadrashta) wrote in message news:<[EMAIL PROTECTED]>...
> Hi Dave,
> 
> I suspected that this was the case. I did a simple simulation using
> normal random numbers, rescaled using a log-transform; indeed, the acf
> shows significant peaks that go on 'forever'.
> I suspect that a nonstationarity in the original time-series is the
> cause of the behavior I see, yet, if the process is nonlinear, then I
> can't justify elminating the nonstationarity which might destroy the (if
> any) nonlinear dynamics of the process that i'm interested in. I'm
> somewhat stuck on what to do here.
> 
> What is considered a reasonable procedure for examining this time-series
> and determining whether it contains nonlinear structure? that is, what
> types of linear analysis should I undertake before trying to look at
> nonlinearity. I realize that one could follow the ARIMA approach and
> attempt to model trends, seasonality, take differences, etc and then fit
> an ARMA model to the resultant stationary process, but if we are
> interested in the nonlinear structure, would this still be the correct
> approach? 
> 
> Any suggestions are appreciated. A recipe for initial analysis is even
> more appreciated.
> 
> regards,
> P
> 
> _____________________________________
> Pradyumna Sribharga Upadrashta, PhD Student
> Scientific Computation, UofMN
> 
Prady,

I myself have not done too much in this area. You might consider
reviewing 


http://pages.stern.nyu.edu/~churvich/TimeSeries/Handouts/NonLin.pdf

and contacting Prof. Stern at NYU .

You might also consider simulating some non-linear models and then
study the implications of pre-filtering with identified ( albeit
incorrectly ) ARIMA structure. I would extend the simulation to 
include various deterministic structures such as local time trends
and stochastic structures such as differencing with a trend which often
masquerades as a linear time trend model.

Hope this helps ...

Dave Reilly
AUTOMATIC FORECASTING SYSTEMS
http://www.autobox.com
.
.
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