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A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions
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September 13, 2010
We propose a new model-based, nonlinear method for seasonally adjusting time series in a multiplicative components model. The method seeks to reduce the bias inherent in linear model-based approaches, while at the same time preserving the flexibility of parametric methods. We discuss the problem of bias and the concept of recovery, and demonstrate the favorable properties of the proposed algorithm on several synthetic series.
Published Online: 2010-9-13
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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