A robust data-driven version of the Berlin Method - Siegfried Heiler; Yuanhua Feng (University of Konstanz, Center of Finance and Econometrics (CoFE), 2000)
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).
urn:nbn:de:bsz:352-opus-4973
Keywords: 330; Dekompositionsverfahren; Zeitreihenanalyse; Robustes Verfahren; Theorie; Saisonbereinigung