IDEAS home Printed from https://ideas.repec.org/p/mol/ecsdps/esdp03004.html
   My bibliography  Save this paper

Forecasting Industrial Production and the Early Detection of Turning Points

Author

Listed:
  • Bruno, Giancarlo
  • Lupi, Claudio

Abstract

In this paper we propose a simple model to forecast industrial production in Italy up to 6 months ahead. We show that the forecasts produced using the model outperform some popular forecasts as well as those stemming from an ARIMA model used as a benchmark and those from some single equation alternative models. We show how 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

  • Bruno, Giancarlo & Lupi, Claudio, 2003. "Forecasting Industrial Production and the Early Detection of Turning Points," Economics & Statistics Discussion Papers esdp03004, University of Molise, Department of Economics.
    • Handle: RePEc:mol:ecsdps:esdp03004
    as

    Download full text from publisher

    File URL: http://web.unimol.it/progetti/repec/mol/ecsdps/ESDP03004.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Chambers, Marcus J. & Ercolani, Joanne S. & Taylor, A.M. Robert, 2014. "Testing for seasonal unit roots by frequency domain regression," Journal of Econometrics, Elsevier, vol. 178(P2), pages 243-258.
    3. Keen Meng Choy & Hwee Kwan Chow, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Econometric Society 2004 Australasian Meetings 223, Econometric Society.
    4. Shin, Dong Wan & Oh, Man-Suk, 2000. "Semiparametric tests for seasonal unit roots based on a semiparametric feasible GLSE," Statistics & Probability Letters, Elsevier, vol. 50(3), pages 207-218, November.
    5. Paulo Rodrigues & Philip Hans Franses, 2005. "A sequential approach to testing seasonal unit roots in high frequency data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(6), pages 555-569.
    6. Paulo Rodrigues & Denise Osborn, 1999. "Performance of seasonal unit root tests for monthly data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 985-1004.
    7. Hans Franses, Philip & Koehler, Anne B., 1998. "A model selection strategy for time series with increasing seasonal variation," International Journal of Forecasting, Elsevier, vol. 14(3), pages 405-414, September.
    8. Smith, Jeremy & Otero, Jesus, 1997. "Structural breaks and seasonal integration," Economics Letters, Elsevier, vol. 56(1), pages 13-19, September.
    9. Beenstock, Michael & Reingewertz, Yaniv & Paldor, Nathan, 2016. "Testing the historic tracking of climate models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1234-1246.
    10. Osterloh, Steffen, 2018. "How do politics affect economic sentiment? The effects of uncertainty and policy preferences," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181614, Verein für Socialpolitik / German Economic Association.
    11. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    12. del Barrio Castro, Tomás & Osborn, Denise R., 2023. "Periodic Integration and Seasonal Unit Roots," MPRA Paper 117935, University Library of Munich, Germany, revised 2023.
    13. D R Osborn & M Sensier, 2004. "Modelling UK Inflation: Persistence, Seasonality and Monetary Policy," Centre for Growth and Business Cycle Research Discussion Paper Series 46, Economics, The University of Manchester.
    14. Hamori, Shigeyuki, 2001. "Seasonality and stock returns: some evidence from Japan," Japan and the World Economy, Elsevier, vol. 13(4), pages 463-481, December.
    15. Evren Erdoğan Cosar, 2006. "Seasonal behaviour of the consumer price index of Turkey," Applied Economics Letters, Taylor & Francis Journals, vol. 13(7), pages 449-455.
    16. Guglielmo Maria Caporale & Luis Gil-Alana, 2006. "Long memory at the long-run and the seasonal monthly frequencies in the US money stock," Applied Economics Letters, Taylor & Francis Journals, vol. 13(15), pages 965-968.
    17. Svetlana Gurieva & Irina Kuznetsova & Ekaterina Yumkina & Sergey Manichev & Elena Sidorenko, 2017. "Emotional Intelligence and Behavior Styles of Russian Middle Managers in Business Communication," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 679-693.
    18. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    19. Pierzak, Agnieszka, 2010. "Identyfikacja i usuwanie sezonowości ze wskaźników cen towarów i usług konsumpcyjnych w Polsce," MF Working Papers 6, Ministry of Finance in Poland, revised 25 Aug 2010.
    20. Cecchetti, Stephen G & Karras, Georgios, 1994. "Sources of Output Fluctuations during the Interwar Period: Further Evidence on the Causes of the Great Depression," The Review of Economics and Statistics, MIT Press, vol. 76(1), pages 80-102, February.

    More about this item

    Keywords

    Forecasting; VAR Models; Industrial production; Cyclical indicators.;
    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mol:ecsdps:esdp03004. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Claudio Lupi (email available below). General contact details of provider: https://edirc.repec.org/data/dsmolit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.