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Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data

Author

Listed:
  • Bruno Giancarlo

    (ISAE - Institute for Studies and Economic Analyses)

  • Lupi Claudio

    (University of Molise, Dept. SEGeS, Faculty of Economics)

Abstract

In this paper we propose a relatively simple procedure to predict Euro-zone industrial production using mostly data derived from the business surveys of the three major economies within the European Monetary Union (France, Germany, and Italy). The basic idea is that of estimating business cyclical indicators to be used as predictors for the industrial production in France and Germany; as far as Italy is concerned, forecasts are produced using a model that in the recent past proved to be able to produce accurate forecasts up to six months ahead. In order to derive quantitative predictors from the business surveys data and to aggregate the nation-wide forecast into the Euro-zone forecast, we propose using an approach based on dynamic factors and unobserved components models. The resulting forecasts are accurate up to six steps ahead.

Suggested Citation

  • Bruno Giancarlo & Lupi Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," ISAE Working Papers 33, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    • Handle: RePEc:isa:wpaper:33
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    References listed on IDEAS

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    1. G. Cainelli & C. Lupi, 1998. "Aggregazione contemporanea e specificazione econometrica nella stima trimestrale dei conti economici nazionali," Working Papers 319, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. James Mitchell & Richard J. Smith & Martin R. Weale, 2002. "Quantification of Qualitative Firm-Level Survey Data," Economic Journal, Royal Economic Society, vol. 112(478), pages 117-135, March.
    3. Simpson, Paul W & Osborn, Denise R & Sensier, Marianne, 2001. "Forecasting UK Industrial Production over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 405-424, September.
    4. Giancarlo Bruno & Claudio Lupi, 2004. "Forecasting industrial production and the early detection of turning points," Empirical Economics, Springer, vol. 29(3), pages 647-671, September.
    5. Batchelor, R. A., 1981. "Aggregate expectations under the stable laws," Journal of Econometrics, Elsevier, vol. 16(2), pages 199-210, June.
    6. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    7. Roberta Zizza, 2002. "Forecasting the industrial production index for the euro area through forecasts for the main countries," Temi di discussione (Economic working papers) 441, Bank of Italy, Economic Research and International Relations Area.
    8. Giuseppe Parigi & Roberto Golinelli & Giorgio Bodo, 2000. "Forecasting industrial production in the Euro area," Empirical Economics, Springer, vol. 25(4), pages 541-561.
    9. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    10. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    11. Bergstrom, Reinhold, 1995. "The relationship between manufacturing production and different business survey series in Sweden 1968-;1992," International Journal of Forecasting, Elsevier, vol. 11(3), pages 379-393, September.
    12. Brunello, Giorgio & Lupi, Claudio & Ordine, Patrizia, 2000. "Regional Disparities and the Italian NAIRU," Oxford Economic Papers, Oxford University Press, vol. 52(1), pages 146-177, January.
    13. Chan Guk Huh, 1998. "Forecasting industrial production using models with business cycle asymmetry," Economic Review, Federal Reserve Bank of San Francisco, pages 29-41.
    14. Osborn, Denise R. & Heravi, Saeed & Birchenhall, C. R., 1999. "Seasonal unit roots and forecasts of two-digit European industrial production," International Journal of Forecasting, Elsevier, vol. 15(1), pages 27-47, February.
    15. repec:adr:anecst:y:1999:i:54:p:05 is not listed on IDEAS
    16. repec:bla:econom:v:42:y:1975:i:166:p:123-38 is not listed on IDEAS
    17. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    18. Catherine Doz & Fabrice Lenglart, 1999. "Analyse factorielle dynamique : test du nombre de facteurs, estimation et application à l'enquête de conjoncture dans l'industrie," Annals of Economics and Statistics, GENES, issue 54, pages 91-127.
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    Citations

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    Cited by:

    1. Aleksejs Melihovs & Svetlana Rusakova, 2005. "Short-Term Forecasting of Economic Development in Latvia Using Business and Consumer Survey Data," Working Papers 2005/04, Latvijas Banka.
    2. Bilge Pekçaglayan, 2021. "Determinants of Industrial Production in Turkey: ARDL Model," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 71(71-2), pages 435-456, December.
    3. Javier Jareño, 2007. "Opinion-based surveys in the conjunctural analysis of the Spanish economy," Occasional Papers 0706, Banco de España.
    4. Malgarini, Marco & Margani, Patrizia & Martelli, Bianca Maria, 2005. "Re-engineering the ISAE manufacturing survey," MPRA Paper 42440, University Library of Munich, Germany.

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    More about this item

    Keywords

    Forecasting; VAR models; Industrial production; Cyclical Analysis;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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