infocrit {gets} | R Documentation |
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 |
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