Semiparametric Cointegrating Rank Selection
Some convenient limit properties of usual information criteria are given for cointegrating rank selection. Allowing for a non-parametric 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 -> 8. 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 co-integration analysis. Some simulation results on the finite sample performance of the criteria are reported.
Cointegrating rank, Consistency, Information criteria, Model selection, Nonparametric, Short memory, Unit roots
Wiley: 24 months
Cheng, X and Peter C. B. PHILLIPS.
Semiparametric Cointegrating Rank Selection. (2009). Econometrics Journal. 12, (1), S83-S104. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1807