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A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION
Author(s) -
Hurvich Clifford M.,
Tsai ChihLing
Publication year - 1993
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1993.tb00144.x
Subject(s) - akaike information criterion , mathematics , bayesian information criterion , autoregressive model , model selection , statistics , estimator , information criteria , selection (genetic algorithm) , star model , econometrics , autoregressive integrated moving average , computer science , artificial intelligence , time series
. We develop a small‐sample criterion (AIC C ) for the selection of the order of vector autoregressive models. AIC C is an approximately unbiased estimator of the expected Kullback‐Leibler information. Furthermore, AIC C provides better model order choices than the Akaike information criterion in small samples.