Semiparametric Cointegrating Rank Selection

• Main Authors: CHENG, Xu, Peter C. B. PHILLIPS
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Cointegrating rank; Consistency; Information criteria; Model selection; Nonparametric; Short memory; Unit roots; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/1807; https://ink.library.smu.edu.sg/context/soe_research/article/2806/viewcontent/Semiparametric_cointegrating_rank_selection_pv.pdf
• Institution: Singapore Management University

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.