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Seasonal adjustment of an aggregate series using univariate and multivariate basic structural models

Journal Article


Abstract


  • Time series resulting from aggregation of several sub-series can be seasonally adjusted directly

    or indirectly. With model-based seasonal adjustment, the sub-series may also be considered as a

    multivariate system of series and the analysis may be done jointly. This approach has considerable

    advantage 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 mean

    squared errors of the seasonally adjusted aggregate series resulting from each method are compared

    by calculating their relative efficiency. Results indicate that gains are achievable using the mulLivariate

    approach according to the relative values of the parameters of the sub-series.

Publication Date


  • 2011

Citation


  • 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.

Scopus Eid


  • 2-s2.0-84860814185

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1536

Number Of Pages


  • 26

Start Page


  • 179

End Page


  • 205

Volume


  • 5

Issue


  • 2

Abstract


  • Time series resulting from aggregation of several sub-series can be seasonally adjusted directly

    or indirectly. With model-based seasonal adjustment, the sub-series may also be considered as a

    multivariate system of series and the analysis may be done jointly. This approach has considerable

    advantage 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 mean

    squared errors of the seasonally adjusted aggregate series resulting from each method are compared

    by calculating their relative efficiency. Results indicate that gains are achievable using the mulLivariate

    approach according to the relative values of the parameters of the sub-series.

Publication Date


  • 2011

Citation


  • 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.

Scopus Eid


  • 2-s2.0-84860814185

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1536

Number Of Pages


  • 26

Start Page


  • 179

End Page


  • 205

Volume


  • 5

Issue


  • 2