Testing for unit roots in time series with nearly deterministic seasonal variation
针对季节时间序列中单位根检验存在的尺寸扭曲问题，提出基于预白化和可行广义最小二乘估计的修正方法，蒙特卡洛实验表明能有效减小中等样本下的尺寸扭曲。
This paper addresses the problem of testing for the presence of unit autoregressive roots in seasonal time series with negatively correlated moving average components. For such cases, many of the commonly used tests are known to have exact sizes much higher than their nominal significance level. We propose modifications of available test procedures that are based on suitably prewhitened data and feasible generalized least squares estimators. Monte Carlo experiments show that such modifications are successful in reducing size distortions in samples of moderate size.