Monte Carlo Evidence on Cointegration and Causation
通过蒙特卡洛模拟，比较了三种格兰杰因果检验在小样本下的表现，发现基于误差修正模型的似然比检验在样本量较小时效果最好。
The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error‐correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.