Semiparametric cointegrating rank selection
研究了在允许非参数短期记忆成分的情况下，使用单滞后降秩回归进行协整秩选择时，常用信息准则的极限性质，证明了在惩罚系数满足一定条件时准则的弱一致性，并给出了AIC的极限分布。
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 Cn→∞ and Cn/n → 0 as n →∞. 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.