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...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2009
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| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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