"Gus Gassmann" <[EMAIL PROTECTED]> wrote in message [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > Mountain Bikn' Guy wrote: > > > > > > > Note that the "predictive ability" *is* affected in the sense that > > > a prediction interval for the next observation will not have the > > > correct coverage probability, because you have ignored the explanatory > > > effect of the immediately previous observations. > > > > My working assumption is that the explanatory effect of any (originally) > > previous observation is irrelevant. The model does not need to take into > > account the value of previous observations. Given this situation, I would > > think that using time series techniques and methods would be inappropriate > > (even though I do collect the data sequentially over time). > > Not inappropriate. Perhaps "overkill", perhaps not statistically significant, > but certainly appropriate.
Understood. Dave at Autobox made the point that time series analysis can be considered a superset of standard multiple regression methods. > > > > Note that your future observations still arrive /in order/, not > > > shuffled. It doesn't help you to pretend otherwise. > > > > The fact that future values may arrive in order is, again, irrelevant. I > > will use the model to predict a future value out of order (ie, in the same > > way oen would use any linear regression equation) so again, I do not think I > > should/could use time series methods. > > The reason for using time series models is simple: To make autocorrelation > work _for_ you, by improving the forecasts. Just ask yourself this question: > In predicting the next value, will you be able to make a better forecast > (with a tighter forecast interval) knowing the current value? If yes, time > series > analysis should be used. If not, or if you don't care about forecast intervals, > use any method you like. This makes a lot of sense to me. I do not think we can/will get a better forecast using other values of Y. Therefore, I am not going to use Y in the regression equation as I would in autoregression. > > > The dilemma is that time series methods seem inappropriate, but there are > > also violations of most of the assumptions for using std regression methods > > as well unless I take actions to offset these (for example, resorting the > > data). Maybe I need to go completely to non parametric methods... ? > > Why do I get the impression that you are grasping at straws? > What assumptions are you talking about, how are they violated, > and why does re-sorting the data fix whatever problems you got? Resorting doesn't change anything -- this was just an effort to explain my situation. What is happening is that I am/was reading too many conflicting things and therefore, trying to achieve a situation where I would not violate *any* assumptions of any of my statistical techniques -- an impossibility, to be sure.But this discussion has cleared some things up and I now feel comfortable that I do not need to use autoregression techniques even though I have an autocorrelated time series. Maybe Dave at Autobox will post some of his thoughts because they were helpful to me. (Thanks Dave!) > > > . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
