Abstract
A multivariate structural time series model made up of unobserved components such as trends and seasonals is formulated. A homogeneous system, in which any linear combination of the observations follows the same time series process, is shown to correspond to a multivariate structural model in which the covariance matrices of the disturbances are proportional. A homogeneous model is considerably easier to estimate than the more general model and a score test of homogeneity can be constructed in the frequency domain. The finite-sample properties of this test are evaluated in a series of Monte Carlo experiments. Finally, a test of serial correlation for use in homogeneous systems is described.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
