Hi all, I have two time series that I would like to correlate but as they are autocorrelated, I am "pre-whitening" them first by fitting ARIMA models, then correlating their residuals....as described in https://onlinecourses.science.psu.edu/stat510/?q=node/75
However, http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm discusses some issues with ARIMA in R. In particular, for issue 2, if there is any differencing, a constant term is not included, but the solution is to 1) input a series that has already been differenced and not include an order for differencing in the function, or 2) include an xreg argument. I have some sample code: set.seed(66) x = arima.sim(list(order=c(1,1,0), ar=.9), n=100) + 50 y=c(x[-1:-3],x[98:100]) # Method 1 m1a=arima(diff(x),order=c(1,0,0)) m1b=arima(diff(y),order=c(1,0,0)) a=ccf(resid(m1a),resid(m1b)) # Method 2 m2a=arima(x,order=c(1,1,0),xreg=1:length(x)) m2b=arima(y,order=c(1,1,0),xreg=1:length(y)) b=ccf(resid(m2a),resid(m2b)) My question is why do the two methods generate different results for the CCF (one having a peak correlation at lag 4 and the other at lag 3, and the correlations are also of slightly different values)? I am assuming Method 2 is the correct method since it gives the peak correlation at the correct lag (lag 3), but I'm wondering why Method 1 does not work. Could anyone explain this? Thanks in advance! Emily [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org 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.
