Modeling high-dimensional unit-root time series

This paper proposes a new procedure to build factor models for high-dimensional unitroot time series by postulating that a p-dimensional unit-root process is a nonsingular linear transformation of a set of unit-root processes, a set of stationary common factors that are dynamically dependent, and some idiosyncratic white noise components. For the stationary components, we assume that the factor process captures the temporal dependence, and that the idiosyncratic white noise series explains, jointly with the factors, the cross-sectional dependence. The estimation of nonsingular linear loading spaces is carried out in two steps. First, we use an eigenanalysis of a nonnegative definite matrix of the data to separate the unit-root processes from the stationary ones, and a modified method to specify the number of unit roots. We then employ another eigenanalysis and a projected principal component analysis to identify the stationary common factors and the white noise series. We propose a new procedure to specify the number of white noise series and, hence, the number of stationary common factors. We establish asymptotic properties of the proposed method for both fixed and diverging p as the sample size n increases, and use a simulation and a real example to demonstrate the performance of the proposed method in finite samples. We also compare our method with some commonly used ones in the literature regarding the forecast ability of the extracted factors, and find that the proposed method performs well in out-of-sample forecasting of a 508-dimensional PM2.5 series in Taiwan. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a new procedure for building factor models for high-dimensional unit-root time series, which involves a non-singular linear transformation, estimation of stationary common factors, and idiosyncratic white noise components. The proposed method is shown to have good performance in terms of forecasting ability for a 508-dimensional PM2.5 series in Taiwan, compared to other commonly used methods.