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A new mixed multiplicative-additive model for seasonal adjusment

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  • Arz, Stephanus

Abstract

Usually, seasonal adjustment is based on time series models which decompose an unadjusted series into the sum or the product of four unobservable components (trendcycle, seasonal, working-day and irregular components). In the case of clearly weatherdependent output in the west German construction industry, traditional considerations lead to an additive model. However, this results in an over-adjustment of calendar effects. An alternative is a multiplicative-additive mixed model, the estimation of which is illustrated using X-12-ARIMA. Finally, the relevance of the new model is shown by analysing selected time series for different countries.

Suggested Citation

  • Arz, Stephanus, 2006. "A new mixed multiplicative-additive model for seasonal adjusment," Discussion Paper Series 1: Economic Studies 2006,47, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdp1:5196
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Kirchner, Robert, 1999. "Auswirkungen des neuen Saisonbereinigungsverfahrens Census X-12-ARIMA auf die aktuelle Wirtschaftsanalyse in Deutschland," Discussion Paper Series 1: Economic Studies 1999,07, Deutsche Bundesbank.
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    More about this item

    Keywords

    Seasonal adjustment; calendar adjustment; over-adjustment; multiplicative-additive model; X-12-ARIMA;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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