Signal Extraction: How (In)efficient are Model-Based Approaches? An Empirical Study Based on TRAMO/SEATS and Census X-12-ARIMA
AbstractEstimation 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 effcient estimation method which does not rely on forecasts. It is shown that both model-based procedures are subject to heavy model misspeci.cation 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.
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Bibliographic InfoPaper provided by KOF Swiss Economic Institute, ETH Zurich in its series KOF Working papers with number 04-96.
Length: 28 pages
Date of creation: Dec 2004
Date of revision:
Signalextraction; concurrent filter; unit root; amplitude and time delay.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-01-24 (All new papers)
- NEP-ECM-2006-01-24 (Econometrics)
- NEP-FOR-2006-01-24 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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, American Statistical Association, vol. 16(2), pages 127-52, April.
- 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.
- Clements,Michael & Hendry,David, 1998.
"Forecasting Economic Time Series,"
Cambridge Books, Cambridge University Press,
Cambridge University Press, number 9780521632423.
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