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

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  • Yuanhua Feng

    (University of Paderborn)

Abstract

We propose a fast data-driven procedure for decomposing seasonal time series using the Berlin Method, the software used by the German Federal Statistical Office in this context. Formula of the asymptotic optimal bandwidth h_A is obtained. Meth- ods 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 example show that the proposal works very well in the practice and that data-driven bandwidth selection is a very useful tool to improve the Berlin Method. Deep insights into the iterative plug-in rule are also provided.

Suggested Citation

  • Yuanhua Feng, 2010. "An iterative plug-in algorithm for decomposing seasonal time series using the Berlin Method," Working Papers CIE 33, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:33
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    References listed on IDEAS

    as
    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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