  Standard tests for nonlinearity reject the null hypothesis of a Gaussian
linear process whenever the data is non-stationary. Thus, they are not
appropriate to distinguish nonlinearity from non-stationarity. We address the
problem of generating proper surrogate data corresponding to the null
hypothesis of an ARMA process with slowly varying coefficients.
