Consistent Nonparametric Entropy-Based Testing
利用Kullback-Leibler信息准则，通过非参数密度估计构建检验统计量，用于嵌套假设的单侧检验，并证明其一致性。重点应用于时间序列的序列独立性检验，包括汇率序列的随机游走假设检验。
The Kullback-Leibler information criterion is used as a basis for one-sided testing of nested hypotheses. No distributional form is assumed, so nonparametric density estimation is used to form the test statistic. In order to obtain a normal null limiting distribution, a form of weighting is employed. The test is also shown to be consistent against a class of alternatives. The exposition focusses on testing for serial independence in time series, with a small application to testing the random walk hypothesis for exchange rate series, and tests of some other hypotheses of econometric interest are briefly described.