Abstract:
We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to nonstationary time series. The unobservable (nonstationary) factors are identified via expanding the innovation space step by step; therefore solving a high-dimensional optimization problem by many low-dimensional sub-problems. Asymptotic properties of the estimation were investigated. The proposed methodology was illustrated with both simulated and real data sets.