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An iterative plug-in algorithm for decomposing seasonal time series using the Berlin Method

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  • Yuanhua Feng

Abstract

We propose a fast data-driven procedure for decomposing seasonal time series using the Berlin Method, the procedure used, e.g. by the German Federal Statistical Office in this context. The formula of the asymptotic optimal bandwidth h A is obtained. Methods for estimating the unknowns in h A are proposed. The algorithm is developed by adapting the well-known iterative plug-in idea to time series decomposition. Asymptotic behaviour of the proposal is investigated. Some computational aspects are discussed in detail. Data examples show that the proposal works very well in practice and that data-driven bandwidth selection offers new possibilities to improve the Berlin Method. Deep insights into the iterative plug-in rule are also provided.

Suggested Citation

  • Yuanhua Feng, 2013. "An iterative plug-in algorithm for decomposing seasonal time series using the Berlin Method," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 266-281, February.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:266-281
    DOI: 10.1080/02664763.2012.740626
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    Cited by:

    1. Jan Beran & Jeremy Näscher & Fabian Pietsch & Stephan Walterspacher, 2024. "Testing for periodicity at an unknown frequency under cyclic long memory, with applications to respiratory muscle training," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(4), pages 705-731, December.

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