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Asymmetries and nonlinearities in Italian macroeconomic fluctuations

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  • Luca Stanca

Abstract

This paper reports the results of an empirical investigation of business cycle asymmetries in the Italian economy. Macroeconomic time series, both annual post-Unity and quarterly post-world war II, are subjected to nonlinearity and asymmetry tests. The dynamics of recessions and expansions are then modelled with threshold autoregressive and Markov-switching models. The paper shows that allowing for two regimes is sufficient to account for the finding of neglected nonlinearity. The results indicate that business cycle asymmetries can provide both an intuitive economic interpretation and a parsimonious representation of nonlinearities in macroeconomic time series.

Suggested Citation

  • Luca Stanca, 1999. "Asymmetries and nonlinearities in Italian macroeconomic fluctuations," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 483-491.
    • Handle: RePEc:taf:applec:v:31:y:1999:i:4:p:483-491
    DOI: 10.1080/000368499324192
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    References listed on IDEAS

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    1. Wesley Clair Mitchell, 1927. "Business Cycles: The Problem and Its Setting," NBER Books, National Bureau of Economic Research, Inc, number mitc27-1.
    2. Wesley Clair Mitchell, 1927. "Introductory pages to "Business Cycles: The Problem and Its Setting"," NBER Chapters, in: Business Cycles: The Problem and Its Setting, pages -23, National Bureau of Economic Research, Inc.
    3. Michael D. Boldin, 1992. "Using switching models to study business cycle asymmetries: 1. overview of methodology and application," Research Paper 9211, Federal Reserve Bank of New York.
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    Cited by:

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    2. Matteo Manera & Alessandro Cologni, 2006. "The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis for the G-7 Countries," Working Papers 2006.29, Fondazione Eni Enrico Mattei.
    3. Costanza Torricelli & Marianna Brunetti, 2006. "Economic activity and Recession Probabilities: spread predictive power in Italy," Computing in Economics and Finance 2006 350, Society for Computational Economics.
    4. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
    5. Martha Misas & María Teresa Ramírez, 2006. "Colombian economic growth under Markov switching regimes with endogenous transition probabilities," Borradores de Economia 2148, Banco de la Republica.
    6. Martha Misas & Maria Teresa Ramirez, 2007. "Depressions in the Colombian economic growth during the twentieth century: a Markov switching regime model," Applied Economics Letters, Taylor & Francis Journals, vol. 14(11), pages 803-808.
    7. Mark J. Holmes & Maghrebi Nabil, 2002. "Non-Linearities, Regime Switching and the Relationship Between Asian Equity and Foreign Exchange Markets," International Economic Journal, Taylor & Francis Journals, vol. 16(4), pages 121-139.
    8. Yaya, OlaOluwa S & Ogbonna, Ephraim A & Furuoka, Fumitaka & Gil-Alana, Luis A., 2019. "A new unit root analysis for testing hysteresis in unemployment," MPRA Paper 96621, University Library of Munich, Germany.
    9. Ming-Yuan Leon Li & Hsiou-Wei William Lin & Rau Hsiu-hua, 2005. "The performance of the Markov-switching model on business cycle identification revisited," Applied Economics Letters, Taylor & Francis Journals, vol. 12(8), pages 513-520.
    10. Sella Lisa, 2008. "Old and New Spectral Techniques for Economic Time Series," Department of Economics and Statistics Cognetti de Martiis. Working Papers 200809, University of Turin.
    11. Manera, Matteo & Cologni, Alessandro, 2006. "The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis," International Energy Markets Working Papers 12121, Fondazione Eni Enrico Mattei (FEEM).

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