Model selection for integrated autoregressive processes of infinite order

We show that Akaike’s Information Criterion (AIC) and its variants are asymptotically efficient in integrated autoregressive processes of infinite order (AR( ∞ )). This result, together with its stationary counterpart established previously in the literature, ensures that AIC can ultimately achieve prediction efficiency in an AR( ∞ ) process, without knowing the integration order.