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Polynomial Cointegration between Stationary Processes with Long Memory

Author

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  • Marco Avarucci

    (SEFeMEQ, University of Rome “Tor Vergata”)

  • Domenico Marinucci

    (Department of Mathematics, University of Rome “Tor Vergata”)

Abstract

In this paper we consider polynomial cointegrating relationships between stationary processes with long range dependence. We express the regression functions in terms of Hermite polynomials and we consider a form of spectral regression around frequency zero. For these estimates, we establish consistency by means of a more general result on continuously averaged estimates of the spectral density matrix at frequency zero.

Suggested Citation

  • Marco Avarucci & Domenico Marinucci, 2007. "Polynomial Cointegration between Stationary Processes with Long Memory," CEIS Research Paper 99, Tor Vergata University, CEIS.
  • Handle: RePEc:rtv:ceisrp:99
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    File URL: ftp://www.ceistorvergata.it/repec/rpaper/No-99.pdf
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    References listed on IDEAS

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    1. repec:nsr:niesrd:181 is not listed on IDEAS
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    4. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2004. "Dating Business Cycles: A Methodological Contribution with an Application to the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(4), pages 537-565, September.
    5. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-252, July.
    6. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
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    Cited by:

    1. Niels Haldrup & Robinson Kruse, 2014. "Discriminating between fractional integration and spurious long memory," CREATES Research Papers 2014-19, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Nonlinear cointegration; Long memory; Hermite polynomials; Spectral regression; Diagram formula.;

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