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Recursive identification, estimation and forecasting of nonstationary economic time series with applications to GNP international data

Author

Listed:
  • Antonio García Ferrer

    (Dpto. de Análisis Económico, Universidad Autónoma de Madrid.)

  • Juan del Hoyo Bernat

    (Dpto. de Análisis Económico, Universidad Autónoma de Madrid.)

  • Peter C. Young

    (Environmental Science Division, Lancaster University, U.K.)

  • Alfonso Novales Cinca

    (Instituto Complutense de Análisis Económico, Universidad Complutense, Madrid.)

Abstract

En este trabajo proponemos un modelo novedoso de componentes no observables para las variaciones en el PNB anual en varios países. El modelo se formula en espacio de los estados y se estima mediante procedimientos recursivos de filtrado y de suavizado con la muestra completa. Se analiza el producto real anual de nueve países a partir del modelo de componentes no observables en sus versiones univariante y de función de transferencia, utilizando en esta última versión la oferta monetaria como indicador adelantado. Se compara el comportamiento de las predicciones de estos modelos con las obtenidas en trabajos anteriores utilizando el mismo conjunto de datos.

Suggested Citation

  • Antonio García Ferrer & Juan del Hoyo Bernat & Peter C. Young & Alfonso Novales Cinca, 1993. "Recursive identification, estimation and forecasting of nonstationary economic time series with applications to GNP international data," Documentos de Trabajo del ICAE 9310, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  • Handle: RePEc:ucm:doicae:9310
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    References listed on IDEAS

    as
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    3. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
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    Keywords

    Annual real output.;

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