BGVAR (version 2.0.1)

AIC.bgvar: Akaike Information Criterion

Description

Computes the Akaike information criterion for an object bgvar.

Usage

# S3 method for bgvar
AIC(object, ..., k = 2)

Arguments

object

an object of class bgvar.

...

additional arguments.

k

the penalty per parameter to be used. Default is set to k=2.

Value

Returns a numeric value with the corresponding AIC.

References

Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. In: B. N. Petro and F. Csaki (eds.), 2nd International Symposium on Information Theory, pp. 267-281.

Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control AC-19, pp. 716-723.

Examples

# NOT RUN {
library(BGVAR)
data(eerData)
model.mn <- bgvar(Data=eerData,W=W.trade0012,plag=2,saves=100,burns=100,prior="MN")
AIC(model.mn)
# }