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Real-Time Signal Extraction: a Shift of Perspective/Extracción de señal en tiempo real: un cambio de perspectiva

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    (Institute of Data Analysis and Process Design. Zurich University of Applied Sciences. Switzerland)

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    Real-time signal extraction (RTSE) concerns the determination of optimal asymmetric filters towards the end of a time series where - otherwise desirable - symmetric filters cannot be applied anymore. The attractiveness of this particular estimation problem resides in the generality of its scope. For illustrative purposes we here stress real-time monitoring of the US-economy as well as multi-step ahead forecasting. Traditionally, the estimation problem addressed by RTSE is tackled in the methodological framework of the classical maximum likelihood paradigm. We here question the adequacy of this general parametric approach. In particular, we review a statistical apparatus - the DFA - consisting of optimization criteria, diagnostics and tests which accounts for alternative user-relevant aspects of the estimation problem. Interestingly, this customization relates to an uncertainty principle which entails a fundamental shift of perspective. As a result, RTSE emerges as an autonomous discipline with proprietary concepts and statistics. With little suggestive power we may interpret the DFA as a generalization of the traditional model-based approach to more general enquiries about the future than the classical one-step ahead inference. La extracción de señal en tiempo real (RTSE, Real-time signal extraction) se refiere al establecimiento de filtros asimétricos óptimos en la parte final de una serie de tiempo donde los filtros simétricos, tan útiles en el resto de la serie, no pueden ser empleados. El atractivo de este problema concreto de estimación reside en la amplitud de su alcance. Como ejemplo ilustrativo, nos centramos aquí en el seguimiento en tiempo real de la economía de los EEUU así como en las predicciones dinámicas (multi-step). Tradicionalmente, el problema de estimación abordado por la RTSE se estudió desde el marco metodológico del paradigma de la máxima verosimilitud clásica. Nosotros discutimos la conveniencia del enfoque paramétrico general, y proponemos, en concreto, un procedimiento estadístico (el DFA) que incluye criterios de optimización, diagnósticos y contrastes que permiten valorar otros aspectos del problema de estimación que son relevantes para el usuario. Es interesante señalar que esta personalización está relacionada con un principio de incertidumbre que trae consigo un cambio sustancial de perspectiva. RTSE, en consecuencia, se plantea como un método de trabajo autónomo con conceptos y herramientas estadísticas propias. Con un poco de imaginación, podríamos interpretar el DFA como una generalización del enfoque tradicional basado en modelos orientada a dar respuestas más amplias sobre el futuro de la serie que las que proporciona la inferencia clásica a un paso (one-step)

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    Article provided by Estudios de Economía Aplicada in its journal Estudios de Economía Aplicada.

    Volume (Year): 28 (2010)
    Issue (Month): (Diciembre)
    Pages: 497-518

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    Handle: RePEc:lrk:eeaart:28_3_1
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    1. McElroy, Tucker & Wildi, Marc, 2013. "Multi-step-ahead estimation of time series models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 378-394.
    2. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
    3. Daniel M. Chin & John F. Geweke & Preston J. Miller, 2000. "Predicting turning points," Staff Report 267, Federal Reserve Bank of Minneapolis.
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