Events

In this paper, we study high-dimensional curve time series with common stochastic trends. We adopt a dual functional factor model structure with a high-dimensional factor model for the observed curve time series and a low-dimensional factor model for the latent curves with common trends. A functional PCA technique is applied to estimate the common stochastic trends and functional factor loadings. Under some regularity conditions, we derive the mean square convergence and limit distribution theory for the developed estimates, allowing the dimension and sample size to jointly diverge to infinity. We also propose an easy-to-implement criterion to consistently select the number of common stochastic trends and further discuss the model estimation when the nonstationary factors are cointegrated. Extensive Monte-Carlo simulation studies and two empirical applications to large-scale temperature curves in Australia and log-price curves of the S&P stocks, respectively, are conducted to illustrate the finite-sample performance of the developed methodology.

This is a joint work with Y. Li and P.C.B. Phillips.