[EMAIL PROTECTED] (David Reilly) wrote in message news:<[EMAIL PROTECTED]>... > [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
oops ... that might be Prof Churvich at the Stern School of Business NYU .. dave r . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
