A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION 论文

1993Journal of Time Series Analysis引用 402
DOI
Statistical Methods and InferenceBayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference
Statistical Methods and Bayesian InferenceStatistical Methods and InferenceBayesian Methods and Mixture Models
相关技术:Statistical Methods and InferenceBayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference
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Abstract. 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.

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Chih‐Ling Tsai

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Clifford M. Hurvich

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Statistical Methods and InferenceBayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference

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