bestModels {artfima} | R Documentation |
ARIMA(p,0,q), ARFIMA(p,0,q) and ARTFIMA(p,0,q) models are fit for various p=0,1,..., and q=0,1,... and the best models according to the BIC criterion are selected.
bestModels(z, parMax = 4, nbest = 4, likAlg = c("exact", "Whittle"),
d=0, ...)
z |
time series data |
parMax |
maximum number of parameters - see Details |
nbest |
number of models in selection |
likAlg |
likelihood method to use |
d |
regular differencing parameter indicating the number of times to difference |
... |
optional arguments for artfima such as lambdaMax |
numPar = K
, where K is
the number of structural models defined by
K = p+q+n(glp)
,
where n(glp) = 0, 1, 2
according as the model is ARIMA, ARFIMA or
ARTFIMA respectively.
These models are ranked according to the AIC/BIC criterion and the best ones are shown.
The plausibility is shown. This is defined for AIC by the eqn
p(AIC) = exp(0.5*(min(AIC)-AIC))
,
where AIC is the vector of AIC values. Similarly for the BIC.
An S3 list object, "bestmodels". Output is provided using the print method for the "bestmodels"
There are often small differences in the likelihood among a group of 5 or more of the best models. So the "exact" and "Whittle" likelihood methods may produce a different ranking of the models. For this reason the "exact" likelihood method may be preferred.
A.I. McLeod
best_glp_models
print.bestmodels
## Not run:
data(ogden)
\dontrun{ #about 10 seconds
bestModels(ogden)
}
## End(Not run)