IDEAS home Printed from https://ideas.repec.org/p/aim/wpaimx/1240.html
   My bibliography  Save this paper

A Smooth Transition Long-Memory Model

Author

Listed:
  • Marcel Aloy

    (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS)

  • Gilles Dufrénot

    (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS, Banque de France and CEPII)

  • Charles Lai Tong

    (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS)

  • Anne Péguin-Feissolle

    (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS)

Abstract

This paper proposes a new fractional model with a time-varying long-memory parameter. The latter evolves nonlinearly according to a transition variable through a logistic function. We present a LR-based test that allows to discriminate between the standard fractional model and our model. We further apply the nonlinear least squares method to estimate the long memory parameter. We present an application to the unemployment rate in the United-States from 1948 to 2012.

Suggested Citation

  • Marcel Aloy & Gilles Dufrénot & Charles Lai Tong & Anne Péguin-Feissolle, 2012. "A Smooth Transition Long-Memory Model," AMSE Working Papers 1240, Aix-Marseille School of Economics, France, revised Dec 2012.
  • Handle: RePEc:aim:wpaimx:1240
    as

    Download full text from publisher

    File URL: http://www.amse-aixmarseille.fr/sites/default/files/_dt/2012/wp_2012_-_nr_40.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Olivier J. Blanchard & Lawrence H. Summers, 1986. "Hysteresis and the European Unemployment Problem," NBER Chapters, in: NBER Macroeconomics Annual 1986, Volume 1, pages 15-90, National Bureau of Economic Research, Inc.
    2. Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
    3. Di Tella, Rafael & MacCulloch, Robert, 2006. "Europe vs America: Institutional hysteresis in a simple normative model," Journal of Public Economics, Elsevier, vol. 90(12), pages 2161-2186, December.
    4. Robert J. Gordon, 1997. "The Time-Varying NAIRU and Its Implications for Economic Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 11-32, Winter.
    5. Anne Peguin-Feissolle & Gilles Dufrénot & Dominique Guegan, 2006. "Changing-regime volatility : A fractionally integrated SETAR model," Working Papers halshs-00410540, HAL.
    6. Perron, Pierre, 1988. "Trends and random walks in macroeconomic time series : Further evidence from a new approach," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 297-332.
    7. Dufrenot, Gilles & Guegan, Dominique & Peguin-Feissolle, Anne, 2005. "Long-memory dynamics in a SETAR model - applications to stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(5), pages 391-406, December.
    8. Lahiani, A. & Scaillet, O., 2009. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," International Journal of Forecasting, Elsevier, vol. 25(2), pages 418-428.
    9. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
    10. Cheng, Ka Ming & Durmaz, Nazif & Kim, Hyeongwoo & Stern, Michael L., 2012. "Hysteresis vs. natural rate of US unemployment," Economic Modelling, Elsevier, vol. 29(2), pages 428-434.
    11. Engelen, Steve & Norouzzadeh, Payam & Dullaert, Wout & Rahmani, Bahareh, 2011. "Multifractal features of spot rates in the Liquid Petroleum Gas shipping market," Energy Economics, Elsevier, vol. 33(1), pages 88-98, January.
    12. Leon-Ledesma, Miguel A, 2002. "Unemployment Hysteresis in the US States and the EU: A Panel Approach," Bulletin of Economic Research, Wiley Blackwell, vol. 54(2), pages 95-103, April.
    13. Gallant, A. Ronald & Hsieh, David & Tauchen, George, 1997. "Estimation of stochastic volatility models with diagnostics," Journal of Econometrics, Elsevier, vol. 81(1), pages 159-192, November.
    14. Blanchard, Olivier J. & Summers, Lawrence H., 1987. "Hysteresis in unemployment," European Economic Review, Elsevier, vol. 31(1-2), pages 288-295.
    15. Dufrenot, Gilles & Guegan, Dominique & Peguin-Feissolle, Anne, 2005. "Modelling squared returns using a SETAR model with long-memory dynamics," Economics Letters, Elsevier, vol. 86(2), pages 237-243, February.
    16. Mohamed Boutahar & Gilles Dufrénot & Anne Peguin-Feissolle, 2008. "A SIMPLE FRACTIONALLY INTEGRATED MODEL WITH A TIME-VARYING LONG MEMORY PARAMETER Dt - [Document de travail n°2008 - 10]," Working Papers halshs-00275254, HAL.
    17. Morten Ørregaard Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 405-443.
    18. Surgailis, Donatas, 2008. "Nonhomogeneous fractional integration and multifractional processes," Stochastic Processes and their Applications, Elsevier, vol. 118(2), pages 171-198, February.
    19. Dahlhaus, R., 1996. "On the Kullback-Leibler information divergence of locally stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 62(1), pages 139-168, March.
    20. Tanaka, Katsuto, 1999. "The Nonstationary Fractional Unit Root," Econometric Theory, Cambridge University Press, vol. 15(4), pages 549-582, August.
    21. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    22. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.
    23. Alvarez-Ramirez, Jose & Alvarez, Jesus & Solis, Ricardo, 2010. "Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern," Energy Economics, Elsevier, vol. 32(5), pages 993-1000, September.
    24. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Massimiliano Caporin & Rangan Gupta, 2017. "Time-varying persistence in US inflation," Empirical Economics, Springer, vol. 53(2), pages 423-439, September.
    2. Boubaker Heni & Canarella Giorgio & Miller Stephen M. & Gupta Rangan, 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    3. Biswajit Patra & Puja Padhi, 2015. "Backtesting of Value at Risk Methodology: Analysis of Banking Shares in India," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 9(3), pages 254-277, August.
    4. Boubaker Heni, 2018. "A Generalized ARFIMA Model with Smooth Transition Fractional Integration Parameter," Journal of Time Series Econometrics, De Gruyter, vol. 10(1), pages 1-20, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boubaker Heni & Canarella Giorgio & Miller Stephen M. & Gupta Rangan, 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    2. Giorgio Canarella & Rangan Gupta & Stephen M. Miller & Stephen K. Pollard, 2019. "Unemployment rate hysteresis and the great recession: exploring the metropolitan evidence," Empirical Economics, Springer, vol. 56(1), pages 61-79, January.
    3. Boubaker Heni, 2018. "A Generalized ARFIMA Model with Smooth Transition Fractional Integration Parameter," Journal of Time Series Econometrics, De Gruyter, vol. 10(1), pages 1-20, January.
    4. Cheng, Ka Ming & Durmaz, Nazif & Kim, Hyeongwoo & Stern, Michael L., 2012. "Hysteresis vs. natural rate of US unemployment," Economic Modelling, Elsevier, vol. 29(2), pages 428-434.
    5. Tolga Omay & Muhammad Shahbaz & Chris Stewart, 2021. "Is there really hysteresis in the OECD unemployment rates? New evidence using a Fourier panel unit root test," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(4), pages 875-901, November.
    6. Norman J. Morin & John M. Roberts, 1999. "Is hysteresis important for U.S. unemployment?," Finance and Economics Discussion Series 1999-56, Board of Governors of the Federal Reserve System (U.S.).
    7. Heni Boubaker & Nadia Sghaier, 2014. "Wavelet based Estimation of Time- Varying Long Memory Model with Nonlinear Fractional Integration Parameter," Working Papers 2014-284, Department of Research, Ipag Business School.
    8. Matteo Lanzafame, 2010. "The nature of regional unemployment in Italy," Empirical Economics, Springer, vol. 39(3), pages 877-895, December.
    9. Vuyokazi Pikoko & Andrew Phiri, 2019. "Is There Hysteresis in South African Unemployment? Evidence from the Post-Recessionary Period," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 15(3), pages 365-387, JUNE.
    10. Omay, Tolga & Shahbaz, Muhammad & Stewart, Chris, 2021. "Is There Really Hysteresis in OECD Countries’ Unemployment Rates? New Evidence Using a Fourier Panel Unit Root Test," MPRA Paper 107691, University Library of Munich, Germany, revised 10 May 2021.
    11. Patrik Barisic & Tibor Kovac, 2022. "The effectiveness of the fiscal policy response to COVID-19 through the lens of short and long run labor market effects of COVID-19 measures," Public Sector Economics, Institute of Public Finance, vol. 46(1), pages 43-81.
    12. Cheng, Ka Ming, 2022. "Doubts on natural rate of unemployment: Evidence and policy implications," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 230-239.
    13. Valerie Cerra & Antonio Fatás & Sweta C. Saxena, 2023. "Hysteresis and Business Cycles," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 181-225, March.
    14. OlaOluwa S. Yaya & Ahamuefula E. Ogbonna & Robert Mudida, 2019. "Hysteresis of unemployment rates in Africa: new findings from Fourier ADF test," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(6), pages 2781-2795, November.
    15. Naveen Srinivasan & Pratik Mitra, 2014. "The European unemployment problem: its cause and cure," Empirical Economics, Springer, vol. 47(1), pages 57-73, August.
    16. Ewing, Bradley T. & Wunnava, Phanindra V., 2001. "Unit roots and structural breaks in North American unemployment rates," The North American Journal of Economics and Finance, Elsevier, vol. 12(3), pages 273-282, November.
    17. Laurence Ball & Joern Onken, 2022. "Hysteresis in unemployment: Evidence from OECD estimates of the natural rate," International Finance, Wiley Blackwell, vol. 25(3), pages 268-284, December.
    18. Farmer, Roger E.A. & Nicolò, Giovanni, 2018. "Keynesian economics without the Phillips curve," Journal of Economic Dynamics and Control, Elsevier, vol. 89(C), pages 137-150.
    19. Antoine d'Autume, 1992. "Coïntégration et modèles dynamiques," Économie et Prévision, Programme National Persée, vol. 106(5), pages 71-83.
    20. Ossama Mikhail & Curtis J. Eberwein & Jagdish Handa, 2003. "The Measurement of Persistence and Hysteresis in Aggregate Unemployment," Method and Hist of Econ Thought 0311002, University Library of Munich, Germany.

    More about this item

    Keywords

    Long-memory; nonlinearity; time varying parameter; logistic.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aim:wpaimx:1240. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gregory Cornu (email available below). General contact details of provider: https://edirc.repec.org/data/amseafr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.