"Mountain Bikn' Guy" <[EMAIL PROTECTED]> wrote in message news:<VmjZ9.63329$Ve4.6714@sccrnsc03>... > My dependent variable fits at least one definition of a time series: "If you > take a sequence of equally spaced readings, this is called a time series." > Furthermore, there is very strong autocorrelation (near 1) in the dependent > variable -- when tested in the order the data is collected. However, I can > randomly resort all the data (dependent plus independent variables) so that > there is no longer any autocorrelation and this does not affect the > predictive ability of the independent variables.
What do you mean ? Are you saying that that the predictive ability ( statistical importance ? ) of the independent variables was zero in both cases or that the form of the resultant transfer function (ARMAX) model was the same in both cases ? . In both of these cases the predictions would be the same . For a discussion of the treatment of causal variables , please see http://www.autobox.com/teach.html Regards Dave Reilly Automatic Forecasting Systems P.S. If you wish you can call me and I will try and sort out your dilemma ... 215-675-0652 So I'm thinking that I am > not dealing with a time series. Any thoughts? > > Any arguments in favor of using time series analyses? > > Knowing that I _can_ remove the autocorrelation, can I proceed to perform > parametric regression analysis without actually randomly sorting the data > and treat this as a non-time-series analysis? > > TIA > > Steve . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
