bestModels: Best BIC Models

In artfima: ARTFIMA Model Estimation

Description

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.

Usage

bestModels(z, parMax = 4, nbest = 4, likAlg = c("exact", "Whittle"), 
     d=0, ...)

Arguments

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

Details

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.

Value

An S3 list object, "bestmodels". Output is provided using the print method for the "bestmodels"

Note

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.

Author(s)

A.I. McLeod

See Also

best_glp_models print.bestmodels

Examples

## Not run: 
data(ogden)
\dontrun{ #about 10 seconds
bestModels(ogden)
}

## End(Not run)

artfima documentation built on May 2, 2019, 1:27 p.m.