Practical Problems with Reduced‐rank ML Estimators for Cointegration Parameters and a Simple Alternative*
指出Johansen降秩最大似然估计协整参数时会产生极端异常值，并用德国货币系统数据说明该问题的实际重要性，提出广义最小二乘系统估计量作为替代，模拟表明后者表现更好。
Abstract Johansen's reduced‐rank maximum likelihood (ML) estimator for cointegration parameters in vector error correction models is known to produce occasional extreme outliers. Using a small monetary system and German data we illustrate the practical importance of this problem. We also consider an alternative generalized least squares (GLS) system estimator which has better properties in this respect. The two estimators are compared in a small simulation study. It is found that the GLS estimator can indeed be an attractive alternative to ML estimation of cointegration parameters.