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.
infocrit(x, method=c("sc","aic","aicc","hq"))info.criterion(logl, n=NULL, k=NULL, method=c("sc","aic","aicc","hq"))
infocrit: a numeric (i.e. the value of the chosen information criterion)
info.criterion: a list with elements
type of information criterion
number of observations
number of parameters
the value on the information criterion
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)
character, either "sc" (default), "aic", "aicc" or "hq"
numeric, the value of the log-likelihood
integer, number of observations
integer, number of parameters
Genaro Sucarrat, http://www.sucarrat.net/
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.
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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
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