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Signal Extraction: How (In)efficient Are Model-Based Approaches? An Empirical Study Based on TRAMO/SEATS and Census X-12-ARIMA

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  • Marc Wildi
  • Bernd Schips

Abstract

Estimation of signals at the current boundary of time series is an important task in many practical applications. In order to apply the symmetric filter at current time, model-based approaches typically rely on forecasts generated from a time series model in order to extend (stretch) the time series into the future. In this paper we analyze performances of concurrent filters based on TRAMO and X-12-ARIMA for business survey data and compare the results to a new efficient estimation method which does not rely on forecasts. It is shown that both model-based procedures are subject to heavy model misspecification related to false unit root identification at frequency zero and at seasonal frequencies. Our results strongly suggest that the traditional modelbased approach should not be used for problems involving multi-step ahead forecasts such as e.g. the determination of concurrent filters.

Suggested Citation

  • Marc Wildi & Bernd Schips, 2004. "Signal Extraction: How (In)efficient Are Model-Based Approaches? An Empirical Study Based on TRAMO/SEATS and Census X-12-ARIMA," KOF Working papers 04-96, KOF Swiss Economic Institute, ETH Zurich.
    • Handle: RePEc:kof:wpskof:04-96
    DOI: 10.3929/ethz-a-004957347
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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. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423.
    4. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, December.
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    Cited by:

    1. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    2. Michael Graff, 2006. "Ein multisektoraler Sammelindikator für die Schweizer Konjunktur," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 142(IV), pages 529-577, December.
    3. Wildi, Marc, 2010. "Real-Time Signal Extraction: a Shift of Perspective/Extracción de señal en tiempo real: un cambio de perspectiva," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 28, pages 497-518, Diciembre.
    4. McElroy, Tucker & Wildi, Marc, 2013. "Multi-step-ahead estimation of time series models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 378-394.
    5. McElroy Tucker & Wildi Marc, 2010. "Signal Extraction Revision Variances as a Goodness-of-Fit Measure," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-32, June.

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    Keywords

    Signalextraction; Concurrent filter; Unit root; Amplitude and time delay;
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