Bootstrap Co‐integration Rank Testing: The Effect of Bias‐Correcting Parameter Estimates
研究了用自助法校正协整秩检验中第一阶段参数估计偏差的方法，通过蒙特卡洛模拟评估有限样本表现，发现该方法能显著提升小样本性能。
Abstract Bootstrap‐based methods for bias‐correcting the first‐stage parameter estimates used in some recently developed bootstrap implementations of co‐integration rank tests are investigated. The procedure constructs estimates of the bias in the original parameter estimates by using the average bias in the corresponding parameter estimates taken across a large number of auxiliary bootstrap replications. A number of possible implementations of this procedure are discussed and concrete recommendations made on the basis of finite sample performance evaluated by Monte Carlo simulation methods. The results show that bootstrap‐based bias‐correction methods can significantly improve the small sample performance of the bootstrap co‐integration rank tests.