Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1151220 | Statistical Methodology | 2006 | 18 Pages |
Abstract
A computationally efficient means of detecting seasonal shifts is described. The proposed diagnostic statistics are generated from the output of a smoothing algorithm associated with the Kalman filter. The method can be applied to any model for a seasonal process that can be cast in state space form. We focus on structural time series that provide a natural framework for modelling seasonal shifts. A Monte Carlo experiment establishes that approximate quantiles for the diagnostic statistics can be generated using an independence assumption.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Jeremy Penzer,