Order selection for vector autoregressive models

Order-selection criteria for vector autoregressive (AR) modeling are discussed. The performance of an order-selection criterion is optimal if the model of the selected order is the most accurate model in the considered set of estimated models: here vector AR models. Suboptimal performance can be a result of underfit or overfit. The Akaike information criterion (AIC) is an asymptotically unbiased estimator of the KuIlback-Leibler discrepancy (KLD) that can be used as an order-selection criterion. AIC is known to suffer from overfit: The selected model order can be greater than the optimal model order. Two causes of overfit are finite sample effects and asymptotic effects. As a consequence of finite sample effects, AIC underestimates the KLD for higher model orders, leading to overfit. Asymptotically, overfit is the result of statistical variations in the order-selection criterion.