Yes, ar and arima are using different estimation methods: arima is mle
whereas the default is method-of-moments.
With such a large ar coefficient the end effects will matter, and the
mle (done by arima or ar.mle or ar(method="mle")) is the more accurate
method since it makes maximal use of the ends of the series.
Using coda (and set.seed(1)):
effectiveSize(as.mcmc(xb))
var1
8.3611
so you have effectively about 8 independent samples and your estimates
(by whatever method) have high variability. Take a much longer
series.
On Thu, 7 Jul 2011, Bert Gunter wrote:
WARNING: The following might be **complete baloney** (and my apologies if so).
Erin:
I hope you get a definitive reply on this from a real expert, but if
memory serves, they might be using two different estimation
algorithms. ar() is just doing Yule-Walker recursive calculation as
described in Box-Jenkins, while arima() is using numerical
optimization. You can probably make them closer by changing
convergence criteria for arima(), which would be a good test for my
"explanation."
Cheers,
Bert
On Thu, Jul 7, 2011 at 7:36 AM, Erin Hodgess <erinm.hodg...@gmail.com> wrote:
Dear R People:
Here is some output from AR and ARIMA functions:
xb <- arima.sim(n=120,model=list(ar=0.85))
xb.ar <- ar(xb)
xb.ar
Call:
ar(x = xb)
Coefficients:
1
0.6642
Order selected 1 sigma^2 estimated as 1.094
xb.arima <- arima(xb,order=c(1,0,0),include.mean=FALSE)
xb.arima
Call:
arima(x = xb, order = c(1, 0, 0), include.mean = FALSE)
Coefficients:
ar1
0.6909
s.e. 0.0668
sigma^2 estimated as 1.04: log likelihood = -172.94, aic = 349.88
My question: shouldn't the ar1 and arima coefficients and sigma^2 be
the same, please? Or at least closer than they are?
Thanks,
Erin
--
Erin Hodgess
Associate Professor
Department of Computer and Mathematical Sciences
University of Houston - Downtown
mailto: erinm.hodg...@gmail.com
______________________________________________
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.
--
Brian D. Ripley, rip...@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
______________________________________________
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.
