A Note on Resampling the Integration Across the Correlation Integral with Alternative Ranges
重新审视了Kočenda提出的非线性检验，发现当序列非高斯时拒绝率偏高，建议每次计算时通过随机置换获取经验分布，并指出使用多个邻近参数范围可提高检验功效，最后重新评估了汇率数据的实证结果。
Abstract This paper reconsiders the nonlinearity test proposed by Ko[cbreve]enda (Ko[cbreve]enda, E. (2001). An alternative to the BDS test: integration across the correlation integral. Econometric Reviews20:337–351). When the analyzed series is non‐Gaussian, the empirical rejection rates can be much larger than the nominal size. In this context, the necessity of tabulating the empirical distribution of the statistic each time the test is computed is stressed. To that end, simple random permutation works reasonably well. This paper also shows, through Monte Carlo experiments, that Ko[cbreve]enda's test can be more powerful than the Brock et al. (Brock, W., Dechert, D., Scheickman, J., LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews15:197–235) procedure. However, more than one range of values for the proximity parameter should be used. Finally, empirical evidence on exchange rates is reassessed.