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A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions

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  • McElroy Tucker S

    (U.S. Census Bureau)

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 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.

Suggested Citation

  • McElroy Tucker S, 2010. "A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-23, September.
    • Handle: RePEc:bpj:sndecm:v:14:y:2010:i:4:n:6
    DOI: 10.2202/1558-3708.1756
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    References listed on IDEAS

    as
    1. Burridge, Peter & Wallis, Kenneth F, 1984. "Unobserved-Components Models for Seasonal Adjustment Filters," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 350-359, October.
    2. McElroy, Tucker & Sutcliffe, Andrew, 2006. "An iterated parametric approach to nonstationary signal extraction," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2206-2231, May.
    3. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    4. Ozaki, Tohru & Thomson, Peter, 2002. "A Non-linear Dynamic Model for Multiplicative Seasonal-Trend Decomposition," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(2), pages 107-124, March.
    5. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    6. McElroy, Tucker, 2008. "Matrix Formulas For Nonstationary Arima Signal Extraction," Econometric Theory, Cambridge University Press, vol. 24(4), pages 988-1009, August.
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    Cited by:

    1. Alexander Dokumentov & Rob J. Hyndman, 2015. "STR: A Seasonal-Trend Decomposition Procedure Based on Regression," Monash Econometrics and Business Statistics Working Papers 13/15, Monash University, Department of Econometrics and Business Statistics.
    2. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.

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