A CONSISTENT NONPARAMETRIC TEST FOR CAUSALITY IN QUANTILE
将郑（1998）的独立数据分位数检验方法扩展到依赖数据，提出检验分位数格兰杰因果关系的一致非参数检验，并证明其渐近正态性，模拟和原油价格、汇率、黄金价格的经济应用展示了检验效果。
This paper proposes a nonparametric test of Granger causality in quantile. Zheng (1998, Econometric Theory 14, 123–138) studied the idea to reduce the problem of testing a quantile restriction to a problem of testing a particular type of mean restriction in independent data. We extend Zheng’s approach to the case of dependent data, particularly to the test of Granger causality in quantile. Combining the results of Zheng (1998) and Fan and Li (1999, Journal of Nonparametric Statistics 10, 245–271), we establish the asymptotic normal distribution of the test statistic under a β -mixing process. The test is consistent against all fixed alternatives and detects local alternatives approaching the null at proper rates. Simulations are carried out to illustrate the behavior of the test under the null and also the power of the test under plausible alternatives. An economic application considers the causal relations between the crude oil price, the USD/GBP exchange rate, and the gold price in the gold market.