Semiparametric Cointegrating Rank Selection
University of Pennsylvania - Department of Economics
Peter C. B. Phillips
Yale University - Cowles Foundation; University of Auckland; University of Southampton; Singapore Management University - School of Economics
May 1, 2008
Cowles Foundation Discussion Paper No. 1658
Some convenient limit properties of usual information criteria are given for cointegrating rank selection. Allowing for a nonparametric short memory component and using a reduced rank regression with only a single lag, standard information criteria are shown to be weakly consistent in the choice of cointegrating rank provided the penalty coefficient C_n -> infinity and C_n/n -> 0 as n -> infinity. The limit distribution of the AIC criterion, which is inconsistent, is also obtained. The analysis provides a general limit theory for semiparametric reduced rank regression under weakly dependent errors. The method does not require the speci cation of a full model, is convenient for practical implementation in empirical work, and is sympathetic with semiparametric estimation approaches to cointegration analysis. Some simulations results on nite sample performance of the criterion are reported.
Number of Pages in PDF File: 24
Keywords: Cointegrating rank, Consistency, Information criteria, Model selection, Nonparametric, Short memory, Unit roots
JEL Classification: C22, C32working papers series
Date posted: May 21, 2008
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