gets (version 0.9)

info.criterion: Computes the Average Value of an Information Criterion

Description

Given a log-likelihood, the number of observations and the number of estimated parameters, the average value of a chosen information criterion is computed. This facilitates comparison of models that are estimated with a different number of observations, e.g. due to different lags.

Usage

info.criterion(logl, n=NULL, k=NULL, method=c("sc", "aic", "aicc", "hq"))

Arguments

logl

numeric, the value of the log-likelihood

n

integer, number of observations

k

integer, number of parameters

method

character, either "sc" (default), "aic", "aicc" or "hq"

Value

Details

Contrary to AIC and BIC, info.criterion computes the average criterion value (i.e. division by the number of observations). This facilitates comparison of models that are estimated with a different number of observations, e.g. due to different lags.

References

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

E. Hannan and B. Quinn (1979): 'The determination of the order of an autoregression'. Journal of the Royal Statistical Society B 41, pp. 190-195

C.M. Hurvich and C.-L. Tsai (1989): 'Regression and Time Series Model Selection in Small Samples'. Biometrika 76, pp. 297-307

G. Schwarz (1978): 'Estimating the dimension of a model'. The Annals of Statistics 6, pp. 461-464