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Forecasting Industrial Production and the Early Detection of Turning Points


  • Giancarlo Bruno

    (Institute for Studies and Economic Analyses)

  • Claudio Lupi

    (Institute for Studies and Economic Analyses & Libera Università Maria SS. Assunta)


In this paper we propose a simple model to forecast industrial production in Italy. We show that the forecasts produced using the model outperform some popular forecasts as well as those stemming from a trading days- and outlier-robust ARIMA model used as a benchmark. We show that the use of appropriately selected leading variables allows to produce up to twelve-step ahead reliable forecasts. We show how and why the use of these forecasts can improve the estimate of a cyclical indicator and the early detection of turning points for the manufacturing sector. This is of paramount importance for short-term economic analysis.

Suggested Citation

  • Giancarlo Bruno & Claudio Lupi, 2001. "Forecasting Industrial Production and the Early Detection of Turning Points," Econometrics 0110004, EconWPA.
  • Handle: RePEc:wpa:wuwpem:0110004
    Note: Type of Document - zipped PDF; prepared on IBM PC ; pages: 38; figures: included

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    References listed on IDEAS

    1. Giancarlo Bruno, 2001. "Seasonal Adjustment of Italian Industrial Production Index using Tramo-Seats," ISAE Working Papers 18, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    2. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
    3. Giuseppe Parigi & Roberto Golinelli & Giorgio Bodo, 2000. "Forecasting industrial production in the Euro area," Empirical Economics, Springer, vol. 25(4), pages 541-561.
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    Cited by:

    1. Malgarini, Marco & Margani, Patrizia & Martelli, Bianca Maria, 2005. "Re-engineering the ISAE manufacturing survey," MPRA Paper 42440, University Library of Munich, Germany.
    2. Francesca Monti, 2008. "Forecast with judgment and models," Working Paper Research 153, National Bank of Belgium.
    3. Costantini, Mauro & Pappalardo, Carmine, 2010. "A hierarchical procedure for the combination of forecasts," International Journal of Forecasting, Elsevier, vol. 26(4), pages 725-743, October.
    4. repec:zag:zirebs:v:20:y:2017:i:1:p:81-99 is not listed on IDEAS
    5. Bruno, Giancarlo & Lupi, Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," MPRA Paper 42332, University Library of Munich, Germany.
    6. Tatiana Cesaroni & Stefano Iezzi, 2017. "The Predictive Content of Business Survey Indicators: Evidence from SIGE," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 75-104, May.
    7. Bruno, Giancarlo & Otranto, Edoardo, 2008. "Models to date the business cycle: The Italian case," Economic Modelling, Elsevier, vol. 25(5), pages 899-911, September.
    8. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    9. Giancarlo Bruno & Edoardo Otranto, 2003. "Dating the Italian Business Cycle: A Comparison of Procedures," Econometrics 0312003, EconWPA.
    10. Luciana Crosilla, 2006. "The seasonality of ISAE business and consumer surveys: methodological aspects and empirical evidence," ISAE Working Papers 68, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    11. Carmine Pappalardo & Gianfranco Piras, 2004. "Vector-Autoregression Approach to Forecast Italian Imports," ISAE Working Papers 42, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    12. Fichtner, Ferdinand & Rüffer, Rasmus & Schnatz, Bernd, 2009. "Leading indicators in a globalised world," Working Paper Series 1125, European Central Bank.
    13. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
    14. Bruno, Giancarlo, 2009. "Non-linear relation between industrial production and business surveys data," MPRA Paper 42337, University Library of Munich, Germany.
    15. Charles S. Bos & Siem Jan Koopman, 2010. "Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production," Tinbergen Institute Discussion Papers 10-017/4, Tinbergen Institute.
    16. repec:onb:oenbwp:y::i:144:b:1 is not listed on IDEAS

    More about this item


    Forecasting; Forecast Encompassing; VAR Models; Industrial Production; Cyclical Indicators;

    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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