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L’indicateur synthétique mensuel d’activité (ISMA) : une révision

Author

Listed:
  • DARNÉ, O.
  • BRUNHES-LESAGE, V.

Abstract

L’ISMA est un des principaux outils de diagnostic conjoncturel de la Banque de France. Publié chaque mois, il estime la croissance du PIB français pour le prochain trimestre, en se basant sur les données d’enquêtes de la Banque de France.

Suggested Citation

  • Darné, O. & Brunhes-Lesage, V., 2007. "L’indicateur synthétique mensuel d’activité (ISMA) : une révision," Bulletin de la Banque de France, Banque de France, issue 162, pages 21-36.
    • Handle: RePEc:bfr:bullbf:2007:162:01
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    References listed on IDEAS

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    1. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    2. Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-1310, November.
    3. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    4. Peter Grasmann & Filip Keereman, 2001. "An indicator-based short-term forecast for quarterly GDP in the euro area," European Economy - Economic Papers 2008 - 2015 154, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    5. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    6. Jennifer L. Castle, 2005. "Evaluating PcGets and RETINA as Automatic Model Selection Algorithms," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 837-880, December.
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    Cited by:

    1. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.

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    More about this item

    Keywords

    Analyse conjoncturelle; prévision du PIB; étalonnages; données d’enquête.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

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