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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.

CITATION:

Source: U.S. Census Bureau, Statistical Research Division

Created: February 21, 2008
Last revised: February 21, 2008


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Source: U.S. Census Bureau | Statistical Research Division | (301) 763-3215 (or chad.eric.russell@census.gov) |   Last Revised: October 08, 2010