Computes the Average Value of an Information Criterion
infocrit.Rd
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.
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) andn
(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
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
Author
Genaro Sucarrat, http://www.sucarrat.net/