In such a case your question can be split into two stages: The first one is to read your file into R data.frame (let say) making sure that all "bad" entries are replaced by NA (missing value). This usually can be done and there are several ways of doing so. Stage two would be to check how the missing values are treated by the function(s) you intend to use. I do not think that all R functions treat them identically. Some of them may have explicit options for treating NA's.
Regards, Moshe. --- [EMAIL PROTECTED] wrote: > On Tue 23 Oct 07, 10:56 AM, Gad Abraham > <[EMAIL PROTECTED]> said: > > caffeine wrote: > >> I'd like to fit an ARMA(1,1) model to some data > (Federal Reserve Bank > >> interest rates) that looks like: > >> ... > >> 30JUN2006, 5.05 > >> 03JUL2006, 5.25 > >> 04JUL2006, N <---- here! > >> 05JUL2006, 5.25 > >> ... > >> One problem is that holidays have that "N" for > their data. As a test, I > >> tried fitting ARMA(1,1) with and without the > holidays deleted. In other > >> words, I fit the above data as well as this data: > >> ... > >> 30JUN2006, 5.05 > >> 03JUL2006, 5.25 > >> 05JUL2006, 5.25 > >> ... > >> and the ARMA coefficients came out different. > My question is: Should I > >> delete all the holidays from my data file? What > exactly does R do with > >> the > >> "N" values in the fit for the ARMA coefficients? > >> As a related question, the weekends don't have > entries (since the FRB is > >> closed on all weekends). Does the fact that my > data is not regularly > >> spaced > >> pose a problem for ARMA fitting? > > > > A few comments: > > > > * Is the time series stationary? You can't fit > ARIMA to nonstationary data. > > > > * One thing you could try is linear regression of > interest rate on time and > > indicator variables for day of week and special > days like holidays. Then > > fit an ARIMA to the regression residuals. > > > > * Any specific reason why ARMA(1,1)? Have you > looked at the acf and pacf of > > the time series? > > > > Cheers, > > Gad > > Hi Gad, > > This is supposed to be more of an exercise in R than > fitting models. Since > the goal is to simply learn R, we're assuming > stationarity. The series is > nearly weakly stationary, but for the purpose of > this exercise, we're to > assume that it is stationary. > > The choice of order is pretty arbitrary too. Just > an exercise in getting to > feel comfortable with R. > > I guess my question is not so much about the FRB > rates themselves but in how > R interprets data. If I have a time series in a > file: > > 1 .5 > 2 .6 > 3 .4 > 4 No data > 5 .3 > 6 .8 > > Would it be appropriate to delete the line that says > "No data"? Or does R > ignore non-numerical data? > > Sorry if my question was misleading! > > Pete > > ______________________________________________ > 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. > ______________________________________________ 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.
