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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.

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  • 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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    1. Heiler, Siegfried & Feng, Yuanhua, 1997. "A bootstrap bandwidth selector for local polynomial fitting," Discussion Papers, Series II 344, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    2. Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
    3. Heiler, Siegfried & Feng, Yuanhua, 1995. "Data-driven optimal decomposition of time series," Discussion Papers, Series II 287, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    4. Beran, Jan & Feng, Yuanhua & Ocker, Dirk, 1999. "SEMIFAR models," Technical Reports 1999,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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