Some limit properties for information based model selection criteria are given in the context of unit root evaluation and various assumptions about initial conditions. Allowing for a nonparametric short memory component, standard information criteria are shown to be weakly consistent for a unit root provided the penalty coefficient Cn?? and Cn/n?0 as n??. Strong consistency holds when Cn/(log logn)3?? under conventional assumptions on initial conditions and under a slightly stronger condition when initial conditions are infinitely distant in the unit root model. The limit distribution of the AIC criterion is obtained.
AIC, consistency, model selection, nonparametric, unit root.
Journal of the Japan Statistical Society
Japan Statistical Society
PHILLIPS, Peter C. B..
Unit Root Model Selection. (2008). Journal of the Japan Statistical Society. 38, (1), 65-74. Research Collection School Of Economics.
Available at: https://ink.library.smu.edu.sg/soe_research/541
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