infocrit: Computes the Average Value of an Information Criterion

In gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods

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

infocrit(x, method=c("sc","aic","aicc","hq"))

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

Arguments

x	a list that contains, at least, three items: logl (a numeric, the log-likelihood), k (a numeric, usually the number of estimated parameters) and n (a numeric, the number of observations)
method	character, either "sc" (default), "aic", "aicc" or "hq"
logl	numeric, the value of the log-likelihood
n	integer, number of observations
k	integer, number of parameters

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.

Value

infocrit: a numeric (i.e. the value of the chosen information criterion)

info.criterion: a list with elements

method	type of information criterion
n	number of observations
k	number of parameters
value	the value on the information criterion

Author(s)

Genaro Sucarrat, http://www.sucarrat.net/

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

Pretis, Felix, Reade, James and Sucarrat, Genaro (2018): 'Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks'. Journal of Statistical Software 86, Number 3, pp. 1-44

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

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