Some Exact Distribution Theory for Maximum Likelihood Estimators of Cointegrating Coefficients in Error Correction Models
推导了误差修正模型中协整系数最大似然估计的精确有限样本分布，发现降秩回归估计量具有柯西型厚尾且无有限整数阶矩，而三角系统表示下的估计量具有矩阵t分布尾部，解释了为何降秩回归估计更易出现极端异常值。
The author derives some exact finite sample disbibutions and characterizes the tail behavior of maximum likelihood estimators of the cointegrating coefficients in error correction models. The reduced rank regression estimator has a distribution with Cauchy-like tails and no finite moments of integer order. The maximum likelihood estimator of the coefficients in a particular triangular system representation has matrix t-distribution tails with finite integer moments to order T - n + r, where T is the sample size, n is the total number of variables, and r is the dimension of cointegration space. This helps explain some recent simulation studies where extreme outliers occur more frequently for the reduced rank regression estimator than for alternative asymptotically efficient procedures based on triangular representation. Copyright 1994 by The Econometric Society.