A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative
Component Decompositions
Tucker McElroy
KEY WORDS: Nonstationary time series, Seasonality, Trends
ABSTRACT
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 °exibility 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.
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