Michael wrote: > Hi all, > > I have some residuals from regression, and i suspect they have correlations > in them... > > I am willing to cast the correlation into a ARMA(p, q) framework, > > what's the best way to identify the most suitable p, and q, and fit ARMA(p, > q) model and then correct for the correlations in regression? > > I know there are functions in R, I have used them before, but I just want to > see if I can do the whole procedure myself, just to improve my understanding > ... > > Please give me some pointers! Thanks a lot
I'm assuming the data is a time series, otherwise ARIMA models might not be applicable here. I think identifying the order of ARIMA models is something of an art, because most real world models aren't as clean and simple as textbook examples. When you have several similar models, each with its own strengths and weaknesses, which one is "best"? In short, you want to make sure your series is stationary, look at its ACF and PACF, then try different values of p and q based on that, and finally look at the residuals (autocorrelation, distribution, etc). This is basically the Box-Jenkins methodology. The most accessible descriptions I've seen are in "Forecasting: Methods and Applications" by Makridakis, Wheelwright and Hyndman (chapter 7), and "Forecasting with Univariate Box-Jenkins Models" by Pankratz. Cheers, Gad -- Gad Abraham Department of Mathematics and Statistics The University of Melbourne Parkville 3010, Victoria, Australia email: [EMAIL PROTECTED] web: http://www.ms.unimelb.edu.au/~gabraham ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.