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Las Opiniones Empresariales Como Predictores De Los Puntos De Giro Del Ciclo Industrial/Forescasting Turning Points of the Industrial Cycle from Business Expectation Surveys



    () (Departamento de Economía Aplicada. Universidad de Oviedo. Avda. del Cristo s/n. Oviedo.Telf: 985105052 Fax: 985104765.)


    () (Departamento de Economía Aplicada. Universidad de Oviedo. Avda. del Cristo s/n. Oviedo.Telf.: 985103759 Fax: 985104765.)


En este trabajo se estudia la utilidad del Indicador de Clima industrial (ICI) para predecir los puntos de giro en los ciclos de la actividad industrial española. La evidencia empírica disponible indica que el Indicador de Clima industrial (ICI) puede adelantar los movimientos de la producción industrial en el corto plazo. Puesto que la presencia de puntos de giro señala cambios de fase en la coyuntura económica es recomendable predecirlos con la mayor anticipación posible para así implementar las medidas de política económica y adoptar estrategias empresariales que se consideren adecuadas en cada caso. En este trabajo, mostramos cómo es posible aumentar el horizonte temporal para predecir los picos y los valles en el Índice de Producción Industrial (IPI) de la economía española a partir del ICI utilizando para ello un algoritmo bayesiano basado en el modelo de probabilidad secuencial recursiva propuesto por Neftçi (1982). The main aim of this paper is to enhance the usefulness of the Industrial Confi dence Indicator (ICI) to forecast turning points of the current business cycle in Spain. Evidence suggests that the Industrial Confidence Indicator (ICI) may lead movements in aggregate output in a very short time horizon. Since the presence of turning points indicates economic changes it is advisable to forecast them as soon as possible in order to design the suitable policy measures or manager strategies. In this work we show how to increase the length of time to forecasts peaks and troughs in the Spanish Industrial Production Index (IPI) from the ICI by using a Bayesian algorithm based on Neftçi’s probability model.

Suggested Citation

  • Moreno Cuartas, Blanca & López Menéndez, Ana Jesús, 2007. "Las Opiniones Empresariales Como Predictores De Los Puntos De Giro Del Ciclo Industrial/Forescasting Turning Points of the Industrial Cycle from Business Expectation Surveys," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 25, pages 511-528, Abril.
  • Handle: RePEc:lrk:eeaart:25_1_19

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    References listed on IDEAS

    1. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-391, July.
    2. McCulloch, J Hutson, 1975. "The Monte Carlo Cycle in Business Activity," Economic Inquiry, Western Economic Association International, vol. 13(3), pages 303-321, September.
    3. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    4. Diebold, Francis X & Rudebusch, Glenn D, 1990. "A Nonparametric Investigation of Duration Dependence in the American Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 98(3), pages 596-616, June.
    5. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, January.
    6. Francis X. Diebold & Glenn D. Rudebusch, 1989. "Forecasting output with the composite leading index: an ex ante analysis," Finance and Economics Discussion Series 90, Board of Governors of the Federal Reserve System (U.S.).
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    More about this item


    Indicador de Clima industrial (ICI); Índice de Producción Industrial (IPI); Puntos de Giro; Fechado cíclico; Probabilidad de Neftçi/Industrial Confidence Indicator (ICI); Industrial Production Index (IPI); Turning points; Cyclical.;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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