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A robust data-driven version of the Berlin Method

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  • Heiler, Siegfried
  • Feng, Yuanhua

Abstract

In this paper a robust data-driven procedure for decomposing seasonal time series based on a generalized Berlin Method (BV, Berliner Verfahren) as proposed by Heiler and Michels (1994) is discussed. The basic robust algorithm used here is an adaptation of the LOWESS (LOcally Weighted Scatterplot Smoothing) procedure (Cleveland, 1979). For selecting the optimal bandwidth the simple double smoothing rule (Heiler and Feng, 1999) is used. The optimal order of the local polynomial is selected with a BIC criterion. The proposed procedure is applied to the macroeconomic time series used in the recent empirical studies carried out by the German Federal Statistical Office (Speth, 1994 and Höpfner, 1998).

Suggested Citation

  • Heiler, Siegfried & Feng, Yuanhua, 2000. "A robust data-driven version of the Berlin Method," CoFE Discussion Papers 00/15, University of Konstanz, Center of Finance and Econometrics (CoFE).
    • Handle: RePEc:zbw:cofedp:0015
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