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Assessing direct and indirect seasonal decomposition in state space
Stockholm University, Faculty of Social Sciences, Department of Statistics.
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2014 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 41, no 9, 2075-2091 p.Article in journal (Refereed) Published
Abstract [en]

The problem of whether seasonal decomposition should be used prior to or after aggregation of time series is quite old. We tackle the problem by using a state-space representation and the variance/covariance structure of a simplified one-component model. The variances of the estimated components in a two-series system are compared for direct and indirect approaches and also to a multivariate method. The covariance structure between the two time series is important for the relative efficiency. Indirect estimation is always best when the series are independent, but when the series or the measurement errors are negatively correlated, direct estimation may be much better in the above sense. Some covariance structures indicate that direct estimation should be used while others indicate that an indirect approach is more efficient. Signal-to-noise ratios and relative variances are used for inference.

Place, publisher, year, edition, pages
2014. Vol. 41, no 9, 2075-2091 p.
Keyword [en]
covariance, signal to noise, efficiency, indirect seasonal adjustment
National Category
Probability Theory and Statistics
URN: urn:nbn:se:su:diva-105400DOI: 10.1080/02664763.2014.909779ISI: 000336368300014OAI: diva2:728705


Available from: 2014-06-24 Created: 2014-06-24 Last updated: 2014-06-24Bibliographically approved

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Thorburn, Daniel
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