Hi
I would like to use arima () to find the best arima model for y time
series. The default in arima apparently is to use conditional sum of
squares to find the starting values and then ML (as described on the
help page).
Now using the default may lead to error messages saying: "non-stationary
ar part in CSS". When changeing the default to "ML" only the
minimization works. As far as I understand, arima doesn't require
stationarity, but apparently CSS does.
Can anyone tell me what exactly the css method does? And why is CSS-ML
the default in R? Out of efficiency reasons? Because ML and ML-CSS gives
the exact same estimates when applied to the same data. I tried to find
out on google but I couldnt' find anything usefull or understandable to
me as a non-statistician.
Here some data that causes the error message:
X<-6.841067, 6.978443, 6.984755, 7.007225, 7.161198, 7.169790, 7.251534,
7.336429, 7.356600, 7.413271, 7.404165, 7.480869, 7.498686, 7.429809,
7.302747, 7.168251,
7.124798, 7.094881, 7.119132, 7.049250, 6.961049, 7.013442, 6.915243,
6.758036, 6.665078, 6.730523, 6.702005, 6.905522, 7.005191, 7.308986)
model.examp<-arima(X,order=c(7,0,0),include.mean=T) # gives an error
model.examp<-arima(X,order=c(7,0,0),include.mean=T,method="ML") #
gives
no error
Any help on this would be most appreciated
Many thanks fo the help
best wishes
Benedikt
