THE POWER OF COINTEGRATION TESTS
提出基于误差修正模型的协整检验统计量，在无协整时近似正态分布，且比传统迪基-富勒检验更有效，并通过蒙特卡洛模拟和英国货币需求实证验证。
A cointegration test statistic based upon estimation of an error correction model can be approximately normally distributed when no cointegration is present. By contrast, the equivalent Dickey-Fuller statistic applied to residuals from a static relationship has a non-standard asymptotic distribution. When cointegration exists, the error-correction test generally is more powerful than the Dickey-Fuller test. These differences arise because the latter imposes a possibly invalid common factor restriction. The issue is general and has ramifications for system-based cointegration tests. Monte Carlo analysis and an empirical study of U.K. money demand demonstrate the differences in power.