IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1612.07903.html
   My bibliography  Save this paper

Long and Short Memory in Economics: Fractional-Order Difference and Differentiation

Author

Listed:
  • Vasily E. Tarasov
  • Valentina V. Tarasova

Abstract

Long and short memory in economic processes is usually described by the so-called discrete fractional differencing and fractional integration. We prove that the discrete fractional differencing and integration are the Grunwald-Letnikov fractional differences of non-integer order d. Equations of ARIMA(p,d,q) and ARFIMA(p,d,q) models are the fractional-order difference equations with the Grunwald-Letnikov differences of order d. We prove that the long and short memory with power law should be described by the exact fractional-order differences, for which the Fourier transform demonstrates the power law exactly. The fractional differencing and the Grunwald-Letnikov fractional differences cannot give exact results for the long and short memory with power law, since the Fourier transform of these discrete operators satisfy the power law in the neighborhood of zero only. We prove that the economic processes with the continuous time long and short memory, which is characterized by the power law, should be described by the fractional differential equations.

Suggested Citation

  • Vasily E. Tarasov & Valentina V. Tarasova, 2016. "Long and Short Memory in Economics: Fractional-Order Difference and Differentiation," Papers 1612.07903, arXiv.org, revised Aug 2017.
  • Handle: RePEc:arx:papers:1612.07903
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1612.07903
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    2. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    3. Luis A. Gil-Alana & Javier Hualde, 2009. "Fractional Integration and Cointegration: An Overview and an Empirical Application," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 10, pages 434-469, Palgrave Macmillan.
    4. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    5. William R. Parke, 1999. "What Is Fractional Integration?," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 632-638, November.
    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. Valentina V. Tarasova & Vasily E. Tarasov, 2016. "Fractional Dynamics of Natural Growth and Memory Effect in Economics," Papers 1612.09060, arXiv.org, revised Jan 2017.
    2. Valentina V. Tarasova & Vasily E. Tarasov, 2017. "Accelerators in macroeconomics: Comparison of discrete and continuous approaches," Papers 1712.09605, arXiv.org.
    3. Vasily E. Tarasov & Valentina V. Tarasova, 2019. "Dynamic Keynesian Model of Economic Growth with Memory and Lag," Mathematics, MDPI, vol. 7(2), pages 1-17, February.
    4. Valentina V. Tarasova & Vasily E. Tarasov, 2017. "Economic Growth Model with Constant Pace and Dynamic Memory," Papers 1701.06299, arXiv.org, revised Apr 2019.
    5. Vasily E. Tarasov, 2019. "On History of Mathematical Economics: Application of Fractional Calculus," Mathematics, MDPI, vol. 7(6), pages 1-28, June.
    6. Vasily E. Tarasov & Valentina V. Tarasova, 2017. "Accelerators in Macroeconomics: Comparison of Discrete and Continuous Approaches," American Journal of Economics and Business Administration, Science Publications, vol. 9(3), pages 47-55, November.

    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. Vasily E. Tarasov, 2019. "On History of Mathematical Economics: Application of Fractional Calculus," Mathematics, MDPI, vol. 7(6), pages 1-28, June.
    2. Vasily E. Tarasov, 2020. "Non-Linear Macroeconomic Models of Growth with Memory," Mathematics, MDPI, vol. 8(11), pages 1-22, November.
    3. Valentina V. Tarasova & Vasily E. Tarasov, 2017. "Concept of dynamic memory in economics," Papers 1712.09088, arXiv.org.
    4. Tarasov, Vasily E. & Tarasova, Valentina V., 2018. "Macroeconomic models with long dynamic memory: Fractional calculus approach," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 466-486.
    5. Pierre Perron & Zhongjun Qu, 2007. "An Analytical Evaluation of the Log-periodogram Estimate in the Presence of Level Shifts," Boston University - Department of Economics - Working Papers Series wp2007-044, Boston University - Department of Economics.
    6. Christos Christodoulou-Volos & Fotios Siokis, 2006. "Long range dependence in stock market returns," Applied Financial Economics, Taylor & Francis Journals, vol. 16(18), pages 1331-1338.
    7. Perron, Pierre & Qu, Zhongjun, 2010. "Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 275-290.
    8. Gil-Alana, Luis A. & Yaya, OlaOluwa S. & Akinsomi, Omokolade & Coskun, Yener, 2020. "How do stocks in BRICS co-move with real estate stocks?," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 93-101.
    9. Robert A. Connolly & Z. Nuray G‹Ner & Kenneth N. Hightower, 2007. "Evidence on the Extent and Potential Sources of Long Memory in U.S. Treasury Security Returns and Yields," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2-3), pages 689-702, March.
    10. Ana Pérez & Esther Ruiz, 2002. "Modelos de memoria larga para series económicas y financieras," Investigaciones Economicas, Fundación SEPI, vol. 26(3), pages 395-445, September.
    11. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    12. Gil-Alana, Luis A. & Monge, Manuel, 2019. "Lithium: Production and estimated consumption. Evidence of persistence," Resources Policy, Elsevier, vol. 60(C), pages 198-202.
    13. J. Eduardo Vera‐Valdés, 2020. "On long memory origins and forecast horizons," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 811-826, August.
    14. Baillie, Richard T. & Chung, Sang-Kuck, 2002. "Modeling and forecasting from trend-stationary long memory models with applications to climatology," International Journal of Forecasting, Elsevier, vol. 18(2), pages 215-226.
    15. Chevillon, Guillaume & Hecq, Alain & Laurent, Sébastien, 2018. "Generating univariate fractional integration within a large VAR(1)," Journal of Econometrics, Elsevier, vol. 204(1), pages 54-65.
    16. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
    17. Carlos Barros & Luis Gil-Alana, 2013. "Inflation Forecasting in Angola: A Fractional Approach," African Development Review, African Development Bank, vol. 25(1), pages 91-104.
    18. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156, January.
    19. L.A. Gil-Alana, 2003. "Testing the Power of a Generalization of the KPSS-Tests against Fractionally Integrated Hypotheses," Computational Economics, Springer;Society for Computational Economics, vol. 22(1), pages 23-38, August.
    20. Chevillon, Guillaume & Hecq , Alain & Laurent, Sébastien, 2015. "Long Memory Through Marginalization of Large Systems and Hidden Cross-Section Dependence," ESSEC Working Papers WP1507, ESSEC Research Center, ESSEC Business School.

    More about this item

    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:arx:papers:1612.07903. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.