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Breaks or long memory behaviour : an empirical investigation

Author

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  • Lanouar Charfeddine

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

  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

Are structural breaks models true switching models or long memory processes ? The answer to this question remain ambiguous. A lot of papers, in recent years, have dealt with this problem. For instance, Diebold and Inoue (2001) and Granger and Hyung (2004) show, under specific conditions, that switching models and long memory processes can be easily confused. In this paper, using several generating models like the mean-plus-noise model, the STOchastic Permanent BREAK model, the Markov switching model, the TAR model, the sign model and the Structural CHange model (SCH) and several estimation techiques like the GPH technique, the Exact Local Whittle (ELW) and the Wavelet methods, we show that, if the answer is quite simple in some cases, it can be mitigate in other cases. Using French and American inflation rates, we show that these series cannot be characterized by the same class of models. The main result of this study suggests that estimating the long memory parameter without taking account existence of breaks in the data sets may lead to misspecification and to overestimate the true parameter.

Suggested Citation

  • Lanouar Charfeddine & Dominique Guegan, 2012. "Breaks or long memory behaviour : an empirical investigation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00722032, HAL.
  • Handle: RePEc:hal:cesptp:halshs-00722032
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00722032
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    Cited by:

    1. Charfeddine, Lanouar & Benlagha, Noureddine, 2016. "A time-varying copula approach for modelling dependency: New evidence from commodity and stock markets," Journal of Multinational Financial Management, Elsevier, vol. 37, pages 168-189.
    2. 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.
    3. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    4. 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.
    5. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    6. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
    7. Dominique Guégan, 2009. "A Meta-Distribution for Non-Stationary Samples," CREATES Research Papers 2009-24, Department of Economics and Business Economics, Aarhus University.
    8. Charfeddine, Lanouar, 2014. "True or spurious long memory in volatility: Further evidence on the energy futures markets," Energy Policy, Elsevier, vol. 71(C), pages 76-93.
    9. Rahman, Abdul & Khan, Muhammad Arshad & Charfeddine, Lanouar, 2021. "Regime-specific impact of financial reforms on economic growth in Pakistan," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 161-182.
    10. José M. Belbute & Alfredo Marvão Pereira, 2016. "Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption," Working Papers 170, Department of Economics, College of William and Mary.
    11. Charfeddine, Lanouar & Khediri, Karim Ben & Mrabet, Zouhair, 2019. "The forward premium anomaly in the energy futures markets: A time-varying approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 600-615.
    12. repec:ipg:wpaper:2014-503 is not listed on IDEAS
    13. Charfeddine, Lanouar & Khediri, Karim Ben & Aye, Goodness C. & Gupta, Rangan, 2018. "Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 632-647.
    14. Rinke, Saskia & Busch, Marie & Leschinski, Christian, 2017. "Long Memory, Breaks, and Trends: On the Sources of Persistence in Inflation Rates," Hannover Economic Papers (HEP) dp-584, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    15. Charfeddine, Lanouar & Maouchi, Youcef, 2019. "Are shocks on the returns and volatility of cryptocurrencies really persistent?," Finance Research Letters, Elsevier, vol. 28(C), pages 423-430.
    16. Al-Shboul, Mohammad & Anwar, Sajid, 2016. "Fractional integration in daily stock market indices at Jordan's Amman stock exchange," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 16-37.
    17. Canarella, Giorgio & Miller, Stephen M., 2017. "Inflation targeting and inflation persistence: New evidence from fractional integration and cointegration," Journal of Economics and Business, Elsevier, vol. 92(C), pages 45-62.

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    More about this item

    Keywords

    inflation series; Structural breaks models; spurious long memory behavior; inflation series.; Modèles avec ruptures; spurious longue mémoire; inflation.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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