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Characterizing Asymmetries in Business Cycles Using Smooth-Transition Structural Time-Series Models

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  • Proietti Tommaso

    (Università di Udine)

Abstract

This paper aims at testing and modeling business-cycle asymmetries within a structural time-series framework, allowing for smooth transition in the parameters characterizing the cyclical component, namely, the damping factor and the frequency. An LM test of linearity is derived, and illustrations are provided with reference to a set of quarterly U.S. industrial production series for two-digit manufacturing industries.

Suggested Citation

  • Proietti Tommaso, 1998. "Characterizing Asymmetries in Business Cycles Using Smooth-Transition Structural Time-Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(3), pages 1-18, October.
    • Handle: RePEc:bpj:sndecm:v:3:y:1998:i:3:n:2
    DOI: 10.2202/1558-3708.1045
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    Cited by:

    1. Lanouar Charfeddine & Dominique Guegan, 2008. "Is it possible to discriminate between different switching regressions models? An empirical investigation," PSE-Ecole d'économie de Paris (Postprint) halshs-00368358, HAL.
    2. Laurent Ferrara & Dominique Guégan, 2006. "Detection of the Industrial Business Cycle using SETAR Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 353-371.
    3. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    4. Monica Billio & Laurent Ferrara & Dominique Guegan & Gian Luigi Mazzi, 2009. "Evaluation of Nonlinear time-series models for real-time business cycle analysis of the Euro," Documents de travail du Centre d'Economie de la Sorbonne 09053, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. Tommaso Proietti, 2003. "Leave‐K‐Out Diagnostics In State‐Space Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 221-236, March.
    6. Kenneth O. Cogger, 2010. "Nonlinear multiple regression methods: a survey and extensions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(1), pages 19-39, January.
    7. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, Department of Economics and Business Economics, Aarhus University.
    8. Laurent Ferrara & Dominique Guegan, 2006. "Real-time detection of the business cycle using SETAR models," Post-Print halshs-00185372, HAL.
    9. Monika Jeziorska-Pąpka & Magdalena Osińska & Maciej Witkowski, 2005. "Forecasting Returns Using Threshold Models," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), Acta Universitatis Lodziensis. Folia Oeconomica nr 192/2005 - Issues in Modeling, Forecasting and Decision-Making in Financial Markets, edition 1, volume 127, chapter 8, pages 129-142, University of Lodz.
    10. Monica Billio & Massimiliano Caporin & Guido Cazzavillan, 2008. "Dating EU15 monthly business cycle jointly using GDP and IPI," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(3), pages 333-366.
    11. Marian Vavra, 2016. "Testing the Validity of Assumptions of UC-ARIMA Models for Trend-Cycle Decompositions," Working and Discussion Papers WP 4/2016, Research Department, National Bank of Slovakia.
    12. Paresh Kumar Narayan & Seema Narayan, 2008. "Examining The Asymmetric Behaviour Of Macroeconomic Aggregates In Asian Economies," Pacific Economic Review, Wiley Blackwell, vol. 13(5), pages 567-574, December.
    13. Garcia-Ferrer, Antonio & Queralt, Ricardo & Blazquez, Cristina, 2001. "A growth cycle characterisation and forecasting of the Spanish economy: 1970-1998," International Journal of Forecasting, Elsevier, vol. 17(3), pages 517-532.
    14. Laurent Ferrara & Dominique Guegan, 2006. "Real-time detection of the business cycle using SETAR models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00185372, HAL.

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