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Article
Seasonal adjustment of an aggregate series using univariate and multivariate basic structural models
Faculty of Informatics - Papers (Archive)
  • David G Steel, University of Wollongong
  • Yan-Xia Lin, University of Wollongong
  • Carole Birrell, University of Wollongong
RIS ID
52194
Publication Date
1-1-2011
Publication Details

Birrell, C. L., Steel, D. G. & Lin, Y. (2011). Seasonal adjustment of an aggregate series using univariate and multivariate basic structural models. Journal of Statistical Theory and Practice, 5 (2), 179-205.

Abstract

Time series resulting from aggregation of several sub-series can be seasonally adjusted directlyor indirectly. With model-based seasonal adjustment, the sub-series may also be considered as amultivariate system of series and the analysis may be done jointly. This approach has considerableadvantage over the indirect method, as it utilises the covariance structure between the sub-series.This paper compares a model-based univariate and multivariate approach to seasonal adjustment.Firstly, the univariate basic structural model (BSM) is applied directly to the aggregate series. Secondly,the multivariate BSM is applied to a transformed system of sub-series. The prediction meansquared errors of the seasonally adjusted aggregate series resulting from each method are comparedby calculating their relative efficiency. Results indicate that gains are achievable using the mulLivariateapproach according to the relative values of the parameters of the sub-series.

Citation Information
David G Steel, Yan-Xia Lin and Carole Birrell. "Seasonal adjustment of an aggregate series using univariate and multivariate basic structural models" (2011) p. 179 - 205
Available at: http://works.bepress.com/yan-xia_lin/1/