Complex Reduced Rank Models For Seasonally Cointegrated Time Series
提出一种新的季节性协整变量表示法：复杂误差修正模型，通过降秩回归进行统计推断，估计量和检验统计量渐近等价于最大似然法，蒙特卡洛模拟评估了小样本性质，并给出实证示例。
This paper introduces a new representation for seasonally cointegrated variables, namely the complex error correction model, which allows statistical inference to be performed by reduced rank regression. The suggested estimators and tests statistics are asymptotically equivalent to their maximum likelihood counterparts. The small sample properties are evaluated by a Monte Carlo study and an empirical example is presented to illustrate the concepts and methods.