Structural changes estimation for strongly dependent processes
In this paper, we consider the problem of estimating multiple structural breaks in a long-memory fractional autoregressive integrated moving-average time series. The number of break points as well as their locations, the orders and the parameters of each regime are assumed to be unknown. A selection criterion based on the minimum description length principle is proposed and a genetic algorithm is implemented for its optimization. Monte Carlo simulation results show the effectiveness of this criterion and an application to the Nile River data is considered.
structural change long-memory minimum description length principle genetic algorithm non-stationary time series