Power of surrogate data testing with respect to nonstationarity

Surrogate data testing is a method frequently applied to evaluate the results of nonlinear time series analysis. Since the null hypothesis tested against is a linear, Gaussian, stationary stochastic process a positive outcome may not only result from an underlying nonlinear or even chaotic system, but also from, e.g., a nonstationary linear one. We investigate the power of the test against nonstationarity.