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Dating the Italian Business Cycle: A Comparison of Procedures

Author

Listed:
  • Giancarlo Bruno

    (ISAE-Roma)

  • Edoardo Otranto

    (DEIR-Università di Sassari)

Abstract

The problem of dating the business cycle has recently received many contributions, with a lot of proposed statistical methodologies, parametric and non parametric. Despite of this, only a few countries produce an official dating of the business cycle. In this work we try to apply some procedures for an automatic dating of the Italian business cycle in the last thirty years, checking differences among various methodologies and with the ISAE chronology. To this end parametric as well as non parametric methods are employed. The analysis is carried out both aggregating results from single time series and directly in a multivariate framework. The different methods are also evaluated with respect to their ability to timely track turning points. KEYWORDS: signal extraction, turning points, parametric methods, nonparametric methods

Suggested Citation

  • Giancarlo Bruno & Edoardo Otranto, 2003. "Dating the Italian Business Cycle: A Comparison of Procedures," Econometrics 0312003, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0312003
    Note: Type of Document - pdf; prepared on Win98; to print on A4 paper; pages: 20. pdf document submitted via ftp
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    References listed on IDEAS

    as
    1. 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.
    2. Artis, Michael J & Kontolemis, Zenon G & Osborn, Denise, 1995. "Classical Business Cycles for G7 and European Countries," CEPR Discussion Papers 1137, C.E.P.R. Discussion Papers.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
    5. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    6. 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.
    7. Zacharias Psaradakis & Nicola Spagnolo, 2003. "On The Determination Of The Number Of Regimes In Markov-Switching Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 237-252, March.
    8. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
    9. Artis, Michael J & Kontolemis, Zenon G & Osborn, Denise R, 1997. "Business Cycles for G7 and European Countries," The Journal of Business, University of Chicago Press, vol. 70(2), pages 249-279, April.
    10. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
    11. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    12. Edoardo Otranto & Giampiero Gallo, 2002. "A Nonparametric Bayesian Approach To Detect The Number Of Regimes In Markov Switching Models," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 477-496.
    13. 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, October.
    14. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
    15. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388.
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    Citations

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    Cited by:

    1. Mario Quagliariello, 2007. "Banks' riskiness over the business cycle: a panel analysis on Italian intermediaries," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 119-138.
    2. Sergey V. Smirnov & Nikolai V. Kondrashov & Anna V. Petronevich, 2016. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," HSE Working papers WP BRP 122/EC/2016, National Research University Higher School of Economics.
    3. Craigwell, Roland & Maurin, Alain, 2007. "A sectoral analysis of Barbados’ GDP business cycle," MPRA Paper 33428, University Library of Munich, Germany.
    4. Marianna Brunetti & Costanza Torricelli, 2009. "Economic activity and recession probabilities: information content and predictive power of the term spread in Italy," Applied Economics, Taylor & Francis Journals, vol. 41(18), pages 2309-2322.
    5. repec:eee:jmacro:v:61:y:2019:i:c:16 is not listed on IDEAS
    6. Francesco Daveri & Cecilia Jona-Lasinio, 2005. "Italy's Decline: Getting the Facts Right," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 64(4), pages 365-410, December.
    7. Catherine Doz & Anna Petronevich, 2015. "Dating Business Cycle Turning Points for the French Economy: a MS-DFM approach," Documents de travail du Centre d'Economie de la Sorbonne 15009, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    8. Mario Quagliariello, "undated". "Banks' Performance over the Business Cycle: A Panel Analysis on Italian Intermediaries," Discussion Papers 04/17, Department of Economics, University of York.
    9. Silvana Bartoletto & Bruno Chiarini & Elisabetta Marzano & Paolo Piselli, 2015. "Business Cycles, Credit Cycles and Bank Holdings of Sovereign Bonds: Historical Evidence for Italy 1861-2013," CESifo Working Paper Series 5318, CESifo Group Munich.
    10. Mario Quagliariello, 2006. "Banks� Riskiness Over the Business Cicle: a Panel Analysis on Italian Intermediaries," Temi di discussione (Economic working papers) 599, Bank of Italy, Economic Research and International Relations Area.
    11. Stefano Fachin & Andrea Gavosto, 2010. "Trends of labour productivity in Italy: a study with panel co-integration methods," International Journal of Manpower, Emerald Group Publishing, vol. 31(7), pages 755-769, October.
    12. repec:spr:jbuscr:v:13:y:2017:i:1:d:10.1007_s41549-017-0014-9 is not listed on IDEAS
    13. Märten Kress, 2004. "Lending cycles in Estonia," Bank of Estonia Working Papers 2004-3, Bank of Estonia, revised 10 Oct 2004.
    14. Edoardo Otranto, 2005. "Extraction of Common Signal from Series with Different Frequency," Econometrics 0502011, University Library of Munich, Germany.
    15. repec:lrk:eeaart:35_2_4 is not listed on IDEAS
    16. Silvia Palasca & Elisabeta Jaba, 2014. "Leading and Lagging Indicators Of the Economic Crisis," Romanian Statistical Review, Romanian Statistical Review, vol. 62(3), pages 31-47, September.
    17. Fachin, Stefano & Gavosto, Andrea, 2007. "The decline in Italian productivity: a study in estimation of long-Run trends in Total Factor Productivity with panel cointegration methods," MPRA Paper 3112, University Library of Munich, Germany.
    18. Catherine Doz & Anna Petronevich, 2015. "Dating Business Cycle Turning Points for the French Economy: a MS-DFM approach," Documents de travail du Centre d'Economie de la Sorbonne 15009, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

    More about this item

    Keywords

    signal extraction; turning points; parametric methods; nonparametric methods;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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