Inference in Models with Nearly Integrated Regressors
研究了当回归变量的最大自回归根未知时，检验x是否预测y的回归测试，发现现有两步法存在大尺寸扭曲，提出了基于Bonferroni和Scheffe方法的替代程序，且保守检验的功率损失较小。
This paper examines regression tests of whether x forecasts y when the largest autoregressive root of the regressor is unknown. It is shown that previously proposed two-step procedures, with first stages that consistently classify x as I(1) or I(0), exhibit large size distortions when regressors have local-to-unit roots, because of asymptotic dependence on a nuisance parameter that cannot be estimated consistently. Several alternative procedures, based on Bonferroni and Scheffe methods, are therefore proposed and investigated. For many parameter values, the power loss from using these conservative tests is small.