Testing for distributional change in time series

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.