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Which is the best model for the US inflation rate : a structural changes model or a long memory process ?

Author

Listed:
  • Lanouar Charfeddine

    () (OEP - UPEM - Université Paris-Est Marne-la-Vallée)

  • Dominique Guegan

    () (CES - Centre d'économie de la Sorbonne - CNRS - Centre National de la Recherche Scientifique - UP1 - Université Panthéon-Sorbonne)

Abstract

This paper analyzes the dynamics of the US inflation series using two classes of models : structural changes models and Long memory processes. For the first class, we use the Markov Switching (MS-AR) model of Hamilton (1989) and the Structural Change (SCH-AR) model using the sequential method proposed by Bai and Perron (1998, 2003). For the second class, we use the ARFIMA process developed by Granger and Joyeux (1980). Moreover, we investigate whether the observed long memory behavior is a true behavior or a spurious behavior created by the presence of breaks in time series. Our empirical results provide evidence for changes in mean, breaks dates coincide exactly with some economic and financial events such Vietnam War and the two oil price shocks. Moreover, we show that the observed long memory behavior is spurious and is due to the presence of breaks in data set.

Suggested Citation

  • Lanouar Charfeddine & Dominique Guegan, 2007. "Which is the best model for the US inflation rate : a structural changes model or a long memory process ?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00188309, HAL.
  • Handle: RePEc:hal:cesptp:halshs-00188309
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00188309
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    Citations

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    Cited by:

    1. Charfeddine, Lanouar & Guégan, Dominique, 2012. "Breaks or long memory behavior: An empirical investigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5712-5726.
    2. Charfeddine, Lanouar, 2016. "Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis," Economic Modelling, Elsevier, vol. 53(C), pages 354-374.
    3. Dominique Guegan & Philippe de Peretti, 2011. "Tests of Structural Changes in Conditional Distributions with Unknown Changepoints," Documents de travail du Centre d'Economie de la Sorbonne 11042, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    4. Dominique Guégan & Philippe Peretti, 2013. "An omnibus test to detect time-heterogeneity in time series," Computational Statistics, Springer, vol. 28(3), pages 1225-1239, June.
    5. Slim Chaouachi & Zied Ftiti & Frederic Teulon, 2014. "Explaining the Tunisian Real Exchange: Long Memory versus Structural Breaks," Working Papers 2014-147, Department of Research, Ipag Business School.
    6. Peter Smith, 2010. "Discussion of the Fisher Effect Puzzle: A Case of Non-Linear Relationship," Open Economies Review, Springer, vol. 21(1), pages 105-108, February.
    7. repec:ipg:wpaper:2014-503 is not listed on IDEAS
    8. Lanouar Charfeddine & Dominique Guegan, 2012. "Breaks or long memory behaviour : an empirical investigation," Working Papers halshs-00722032, HAL.
    9. Mihaela SIMIONESCU, 2016. "The Identification Of Inflation Rate Determinants In The Usa Using The Stochastic Search Variable Selection," CES Working Papers, Centre for European Studies, Alexandru Ioan Cuza University, vol. 8(1), pages 171-181, March.
    10. Charfeddine, Lanouar & Khediri, Karim Ben, 2016. "Time varying market efficiency of the GCC stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 487-504.
    11. Dominique Guegan & Philippe De Peretti, 2012. "An Omnibus Test to Detect Time-Heterogeneity in Time Series," Working Papers halshs-00721327, HAL.
    12. repec:eee:eneeco:v:65:y:2017:i:c:p:355-374 is not listed on IDEAS

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