Semiparametric Cointegrating Rank Selection

Xu Cheng and Peter C. B. Phillips ()

No 1658, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University

Abstract: 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 specification 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 finite sample performance of the criterion are reported.

Keywords: Cointegrating rank; Consistency; Information criteria; Model selection; Nonparametric; Short memory; Unit roots (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2008-05
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Published in Econometrics Journal (2009), 12: S83-S104

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Journal Article: Semiparametric cointegrating rank selection (2009)
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