IDEAS home Printed from https://ideas.repec.org/p/cdl/ucsdec/qt9pk546xs.html
   My bibliography  Save this paper

Introduction to M-M Processes

Author

Listed:
  • Granger, Clive W.J.
  • Hyung, Namwon

Abstract

This paper introduces a new type of nonlinear model, the min-max model, and analyzes the properties for a pair of series. Stability conditions of this system are given for the nonlinearly integrated bivariate series. Under these stability conditions, the difference of the two series has a threshold-type nonlinearity. One can construct a threshold error correction model from min-max processes. Neglected nonlinearity tests are applied, to the univariate series and to the system, to detect nonlinearity, and it turns out that the tests using the system have better power. We apply the min-max model to U.S. Treasury bill and commercial paper interest rates. The spread of these interest rates shows a threshold-type nonlinearity, and this model outperforms a linear model in terms of its predictability out-of-sample

Suggested Citation

  • Granger, Clive W.J. & Hyung, Namwon, 1998. "Introduction to M-M Processes," University of California at San Diego, Economics Working Paper Series qt9pk546xs, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt9pk546xs
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/9pk546xs.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Balke, Nathan S & Fomby, Thomas B, 1997. "Threshold Cointegration," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(3), pages 627-645, August.
    2. Corradi, Valentina & Swanson, Norman R. & White, Halbert, 2000. "Testing for stationarity-ergodicity and for comovements between nonlinear discrete time Markov processes," Journal of Econometrics, Elsevier, vol. 96(1), pages 39-73, May.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Christina D. Romer & David H. Romer, 1990. "New Evidence on the Monetary Transmission Mechanism," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 21(1), pages 149-214.
    5. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
    6. Benjamin M. Friedman & Kenneth Kuttner, 1993. "Why Does the Paper-Bill Spread Predict Real Economic Activity?," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 213-254, National Bureau of Economic Research, Inc.
    7. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, March.
    8. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226774886, December.
    9. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    10. Granger, Clive W J, 1995. "Modelling Nonlinear Relationships between Extended-Memory Variables," Econometrica, Econometric Society, vol. 63(2), pages 265-279, March.
    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. Granger Clive W.J., 2008. "Non-Linear Models: Where Do We Go Next - Time Varying Parameter Models?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-11, September.
    2. Lee, O. & Shin, D.W., 2007. "A note on geometric ergodicity of a multiple threshold AR(1) processes on the boundary region with application to integrated m-m processes," Economics Letters, Elsevier, vol. 96(2), pages 226-231, August.

    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. Francis X. Diebold & Glenn D. Rudebusch, 2001. "Five questions about business cycles," Economic Review, Federal Reserve Bank of San Francisco, pages 1-15.
    2. Smant, David / D.J.C., 2002. "Bank credit in the transmission of monetary policy: A critical review of the issues and evidence," MPRA Paper 19816, University Library of Munich, Germany.
    3. João Valle e Azevedo & João Tovar Jalles, 2011. "Rational vs. Professional Forecasts," Working Papers w201114, Banco de Portugal, Economics and Research Department.
    4. Mills, Terence C. & Pepper, Gordon T., 1999. "Assessing the forecasters: an analysis of the forecasting records of the Treasury, the London Business School and the National Institute," International Journal of Forecasting, Elsevier, vol. 15(3), pages 247-257, July.
    5. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.
    6. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    7. Mark Weder, 2006. "A heliocentric journey into Germany's Great Depression," Oxford Economic Papers, Oxford University Press, vol. 58(2), pages 288-316, April.
    8. Liu, Dandan & Jansen, Dennis W., 2007. "Macroeconomic forecasting using structural factor analysis," International Journal of Forecasting, Elsevier, vol. 23(4), pages 655-677.
    9. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    10. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    11. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.
    12. J.D. Hollingworth, 1997. "Leading Indicators of Australian Recessions: Part 2," Economics Discussion / Working Papers 97-17, The University of Western Australia, Department of Economics.
    13. Swanson, Norman R., 2002. "Comments on 'A vector error-correction forecasting model of the US economy'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 599-606, December.
    14. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    15. Rianne Legerstee & Philip Hans Franses, 2015. "Does Disagreement Amongst Forecasters Have Predictive Value?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 290-302, July.
    16. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
    17. Canova, Fabio & Ciccarelli, Matteo, 2004. "Forecasting and turning point predictions in a Bayesian panel VAR model," Journal of Econometrics, Elsevier, vol. 120(2), pages 327-359, June.
    18. Ricardo J. Caballero & Arvind Krishnamurthy, 2006. "Flight to Quality and Collective Risk Management," NBER Working Papers 12136, National Bureau of Economic Research, Inc.
    19. Maximo Camacho & Gabriel Perez-Quiros, 2002. "This is what the leading indicators lead," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 61-80.
    20. Vincent, BODART & Konstantin, KHOLODILIN & Fati, SHADMAN-MEHTA, 2005. "Identifying and Forecasting the Turning Points of the Belgian Business Cycle with Regime-Switching and Logit Models," Discussion Papers (ECON - Département des Sciences Economiques) 2005006, Université catholique de Louvain, Département des Sciences Economiques.

    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:cdl:ucsdec:qt9pk546xs. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/deucsus.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.