Dynamic Orthogonal Components for Multivariate Time Series

• 成果类型: Article
• 署名作者: Matteson, David S.; Tsay, Ruey S.
• 署名单位: Cornell University; University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.tm10616
• 发表日期: 2011
• 页码: 1450-1463
• 关键词: sample properties; factor models; GARCH MODELS; heteroskedasticity; identification; moments

摘要

We introduce dynamic orthogonal components (DOC) for multivariate time series and propose a procedure for estimating and testing the existence of DOCs for a given time series. We estimate the dynamic orthogonal components via a generalized decorrelation method that minimizes the linear and quadratic dependence across components and across time. We then use Ljung-Box type statistics to test the existence of dynamic orthogonal components. When DOCs exist, univariate analysis can be applied to build a model for each component. Those univariate models are then combined to obtain a multivariate model for the original time series. We demonstrate the usefulness of dynamic orthogonal components with two real examples and compare the proposed modeling method with other dimension-reduction methods available in the literature, including principal component and independent component analyses. We also prove consistency and asymptotic normality of the proposed estimator under some regularity conditions. We provide some technical details in online Supplementary Materials.