Bootstrap Testing Linear Restrictions on Cointergrating Vectors
提出用参数Bootstrap方法改进协整向量检验的小样本性质，模拟表明该方法能显著降低似然比检验的尺寸扭曲，适用于小样本实证研究。
We consider a computer-intensive method for inference on cointegrating vectors in maximum likelihood cointegration analysis. Simulation studies show that the size distortion for the asymptotic likelihood ratio test can be considerable for small samples. Itis demonstrated that a parametric bootstrap frequently results in a nearly exact α-level test. Furthermore, response surface regression is used to examine small-sample properties of the asymptotic test. In particular, using an extensive experimental design, in which the data-generating processes are based on empirical models, we describe how the complexity of the model affects the degree of size distortion for given sample size.