Determining the number of factors for high-dimensional time series
Qiang Xia, Rubing Liang, Jianhong Wu, Heung Wong · Statistics and Its Interface · 2018
In this paper, we suggest a new method of determining the number of factors in factor modeling for highdimensional stationary time series. When the factors are of different degree of strength, the eigenvalue-based ratio method of Lam and Yao needs a two-step procedure to estimate the number of factors. As a modification of the method, however, our method only needs a one-step procedure for the determination of the number of factors. The resulted estimator is obtained simply by minimizing the ratio of the contribution of two adjacent eigenvalues. Some asymptotic results are also developed for the proposed method. The finite sample performance of the method is well examined and compared with some competitors in the existing literature by Monte Carlo simulations and a real data analysis.