TESTING FOR DISTRIBUTIONAL CHANGE IN TIME SERIES
提出非参数检验方法检测时间序列的分布变化，通过模拟方法解决临界值依赖未知参数的问题，蒙特卡洛实验验证了有效性，并应用于金融市场稳定性分析。
This paper proposes nonparametric tests of change in the distribution function of a time series. The limiting null distributions of the test statistics depend on a nuisance parameter, and critical values cannot be tabulated a priori. To circumvent this problem, a new simulation-based statistical method is developed. The validity of our simulation procedure is established in terms of size, local power, and test consistency. The finite-sample properties of the proposed tests are evaluated in a set of Monte Carlo experiments, and the distributional stability in financial markets is examined.