EMPIRICAL LIKELIHOOD TEST FOR CAUSALITY OF BIVARIATE AR(1) PROCESSES
提出一种基于剖面经验似然的方法来检验双变量AR(1)过程的因果关系，该方法在存在非平稳过程时依然稳健，无需事先判断非平稳性。
Testing for causality is of critical importance for many econometric applications. For bivariate AR(1) processes, the limit distributions of causality tests based on least squares estimation depend on the presence of nonstationary processes. When nonstationary processes are present, the limit distributions of such tests are usually very complicated, and the full-sample bootstrap method becomes inconsistent as pointed out in Choi (2005, Statistics and Probability Letters 75, 39–48). In this paper, a profile empirical likelihood method is proposed to test for causality. The proposed test statistic is robust against the presence of nonstationary processes in the sense that one does not have to determine the existence of nonstationary processes a priori. Simulation studies confirm that the proposed test statistic works well.