In this paper we suggest a number of statistical tests based on neural network models, that are designed to be powerful against structural breaks in otherwise stationary time series processes while allowing for a variety of nonlinear specifications for the dynamic model underlying them. It is...

In this paper we provide tests for the unit root hypothesis against the occurence of an unspecified number of breaks which may be larger than 2 but smaller that the maximum allowed number of breaks, m, in univariate time series models. The advocated procedure is considerably less computationally...

We consider time series forecasting in the presence of ongoing structural change where both the time-series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially...