A Structural-Factor Approach to Modeling High-Dimensional Time Series and Space-Time Data

This article considers a structural-factor approach to modeling high-dimensional time series and space-time data by decomposing individual series into trend, seasonal, and irregular components. For ease in analyzing many time series, we employ a time polynomial for the trend, a linear combination of trigonometric series for the seasonal component, and a new factor model for the irregular components. The new factor model simplifies the modeling process and achieves parsimony in parameterization. We propose a Bayesian information criterion to consistently select the order of the polynomial trend and the number of trigonometric functions, and use a test statistic to determine the number of common factors. The convergence rates for the estimators of the trend and seasonal components and the limiting distribution of the test statistic are established under the setting that the number of time series tends to infinity with the sample size, but at a slower rate. We study the finite-sample performance of the proposed analysis via simulation, and analyze two real examples. The first example considers modeling weekly PM2.5 data of 15 monitoring stations in the southern region of Taiwan and the second example consists of monthly value-weighted returns of 12 industrial portfolios.