greybox (version 2.0.2)

AICc: Corrected Akaike's Information Criterion and Bayesian Information Criterion

Description

This function extracts AICc / BICc from models. It can be applied to wide variety of models that use logLik() and nobs() methods (including the popular lm, forecast, smooth classes).

Usage

AICc(object, ...)
BICc(object, ...)

Value

This function returns numeric value.

Arguments

object

Time series model.

...

Some stuff.

Author

Ivan Svetunkov,

Details

AICc was proposed by Nariaki Sugiura in 1978 and is used on small samples for the models with normally distributed residuals. BICc was derived in McQuarrie (1999) and is used in similar circumstances.

IMPORTANT NOTE: both of the criteria can only be used for univariate models (regression models, ARIMA, ETS etc) with normally distributed residuals! In case of multivariate models, both criteria need to be modified. See Bedrick & Tsai (1994) for details.

References

• Burnham Kenneth P. and Anderson David R. (2002). Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach. Springer-Verlag New York. DOI: [10.1007/b97636](http://dx.doi.org/10.1007/b97636).

• McQuarrie, A. D. (1999). A small-sample correction for the Schwarz SIC model selection criterion. Statistics & Probability Letters, 44(1), 79–86. [10.1016/S0167-7152(98)00294-6](https://doi.org/10.1016/S0167-7152(98)00294-6).

• McQuarrie A.D., A small-sample correction for the Schwarz SIC model selection criterion, Statistics & Probability Letters 44 (1999) pp.79-86. tools:::Rd_expr_doi("10.1016/S0167-7152(98)00294-6")

• Sugiura Nariaki (1978) Further analysts of the data by Akaike's information criterion and the finite corrections, Communications in Statistics - Theory and Methods, 7:1, 13-26, tools:::Rd_expr_doi("10.1080/03610927808827599")

• Bedrick, E. J., & Tsai, C.-L. (1994). Model Selection for Multivariate Regression in Small Samples. Biometrics, 50(1), 226. tools:::Rd_expr_doi("10.2307/2533213")

See Also

AIC, BIC

Examples

xreg <- cbind(rnorm(100,10,3),rnorm(100,50,5))
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rnorm(100,0,3),xreg,rnorm(100,300,10))
colnames(xreg) <- c("y","x1","x2","Noise")

ourModel <- stepwise(xreg)

AICc(ourModel)
BICc(ourModel)