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Which is the Best Model for the US Inflation Rate: A Structural Change Model or a Long Memory Process?

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  • Charfeddine Lanouar
  • Guégan Dominique

Abstract

: This paper analyzes the dynamics of the US inflation time series using two classes of models: structural change models and long memory processes. For the first class, the Markov Switching Autoregressive (MS-AR) model of Hamilton (1989) and the Structural Change-Autoregressive (SCH-AR) model developed by Bai and Perron (1998 and 2003) are used. For the second class, the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process developed by Granger and Joyeux (1980) is used. Moreover, the nature (true or spurious) of the observed long memory behavior is also investigated. The empirical results provide evidence for changes in mean. Break dates coincide with some economic and financial events, such as the Vietnam War and the two oil price shocks. Moreover, the results reveal that the observed long memory behavior is spurious and is due to the presence of breaks in the data.

Suggested Citation

  • Charfeddine Lanouar & Guégan Dominique, 2011. "Which is the Best Model for the US Inflation Rate: A Structural Change Model or a Long Memory Process?," The IUP Journal of Applied Economics, IUP Publications, vol. 0(1), pages 5-25, January.
    • Handle: RePEc:icf:icfjae:v:10:y:2011:i:1:p:5-25
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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. 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.
    3. 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.
    4. repec:ipg:wpaper:2014-503 is not listed on IDEAS
    5. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(1), pages 1-25, March.
    6. 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.
    7. 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.
    8. Dominique Guegan & Philippe de Peretti, 2011. "Tests of structural changes in conditional distributions with unknown changepoints," Post-Print halshs-00611932, HAL.
    9. Charfeddine, Lanouar & Al Refai, Hisham, 2019. "Political tensions, stock market dependence and volatility spillover: Evidence from the recent intra-GCC crises," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    10. Charfeddine, Lanouar, 2017. "The impact of energy consumption and economic development on Ecological Footprint and CO2 emissions: Evidence from a Markov Switching Equilibrium Correction Model," Energy Economics, Elsevier, vol. 65(C), pages 355-374.
    11. 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.
    12. 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.
    13. Lanouar Charfeddine & Dominique Guegan, 2009. "Breaks or Long Memory Behaviour: An empirical Investigation," Post-Print halshs-00377485, HAL.
    14. 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.
    15. 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.
    16. Mimouni, Karim & Charfeddine, Lanouar & Al-Azzam, Moh'd, 2016. "Do oil producing countries offer international diversification benefits? Evidence from GCC countries," Economic Modelling, Elsevier, vol. 57(C), pages 263-280.
    17. Malinda & Maya & Jo-Hui & Chen, 2022. "Testing for the Long Memory and Multiple Structural Breaks in Consumer ETFs," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-6.
    18. Dominique Guegan & Philippe de Peretti, 2011. "An Omnibus Test to Detect Time-Heterogeneity in Time Series," Post-Print halshs-00560221, HAL.
    19. 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.
    20. Dominique Guegan & Philippe de Peretti, 2012. "An Omnibus Test to Detect Time-Heterogeneity in Time Series," Working Papers halshs-00721327, HAL.

    More about this item

    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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