https://github.com/cran/dse
Tip revision: ab08892d097753cdf4449136af92b9e9fb89871c authored by Paul Gilbert on 01 December 2009, 00:00:00 UTC
version 2009.12-1
version 2009.12-1
Tip revision: ab08892
bestTSestModel.Rd
\name{bestTSestModel}
\alias{bestTSestModel}
\title{Select Best Model}
\description{Select the best model.}
\usage{
bestTSestModel(models, sample.start=10, sample.end=NULL,
criterion='aic', verbose=TRUE)
}
\arguments{
\item{models}{a list of TSestModels.}
\item{sample.start}{the starting point to use for calculating
information criteria.}
\item{sample.end}{the end point to use for calculating
information criteria.}
\item{criterion}{Criterion to be used for model
selection. see \code{informationTestsCalculations}. 'taic' would
be a better default
but this is not available for VAR and ARMA models.}
\item{verbose}{if TRUE then additional information is printed.}
}
\value{A TSestModel}
\details{
Information criteria are calculated and
return the best model from ... according to criterion
models should be a list of TSestModel's.
models[[i]]$estimates$pred is not recalculated but a sub-sample identified by
sample.start and sample.end is used and the likelihood is recalculated.
If sample.end=NULL data is used to the end of the sample.
taic might be a better default selection criteria but it is
not available for ARMA models.
}
\seealso{
\code{\link{estBlackBox1}},
\code{\link{estBlackBox2}}
\code{\link{estBlackBox3}}
\code{\link{estBlackBox4}}
\code{\link[dse]{informationTestsCalculations}}
}
\examples{
data("eg1.DSE.data.diff", package="dse")
models <- list(estVARXls(eg1.DSE.data.diff), estVARXar(eg1.DSE.data.diff))
z <- bestTSestModel(models)
}
\concept{DSE}
\keyword{ts}