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Cointegration in Fractional Systems with Deterministic Trends

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  • Fabrizio Iacone
  • Peter M Robinson

Abstract

We consider a cointegrated system generated by processes that may be fractionally integrated, and by additive polynomial and generalized polynomial trends. In view of the consequent competition between stochastic and deterministic trends, we consider various estimates of the cointegrating vector and develop relevant asymptotic theory, including the situation where fractional orders of integration are unknown.

Suggested Citation

  • Fabrizio Iacone & Peter M Robinson, 2004. "Cointegration in Fractional Systems with Deterministic Trends," STICERD - Econometrics Paper Series 476, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  • Handle: RePEc:cep:stiecm:476
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    Citations

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    Cited by:

    1. Luis Gil-Alana & Rolando Peláez, 2008. "The persistence of earnings per share," Review of Quantitative Finance and Accounting, Springer, vol. 31(4), pages 425-439, November.
    2. Javier Hualde & Morten Ørregaard Nielsen, 2017. "Truncated sum of squares estimation of fractional time series models with deterministic trends," Working Papers 1376, Queen's University, Department of Economics.
    3. Luis A. Gil-Alana, 2009. "Warming break trends and fractional integration in the northern, southern and global temperature anomaly series," Faculty Working Papers 09/09, School of Economics and Business Administration, University of Navarra.
    4. Jiawen Xu & Pierre Perron, 2013. "Robust testing of time trend and mean with unknown integration order errors Frequency (and Other) Contaminations," Boston University - Department of Economics - Working Papers Series 2013-006, Boston University - Department of Economics.
    5. Hualde, J. & Robinson, P.M., 2010. "Semiparametric inference in multivariate fractionally cointegrated systems," Journal of Econometrics, Elsevier, vol. 157(2), pages 492-511, August.
    6. Guglielmo Maria Caporale & Luis Gil‐Alana, 2014. "Long‐Run and Cyclical Dynamics in the US Stock Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(2), pages 147-161, March.
    7. Katarzyna Lasak, 2008. "Maximum likelihood estimation of fractionally cointegrated systems," CREATES Research Papers 2008-53, Department of Economics and Business Economics, Aarhus University.
    8. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
    9. Hualde Javier & Iacone Fabrizio, 2012. "First Stage Estimation of Fractional Cointegration," Journal of Time Series Econometrics, De Gruyter, vol. 4(1), pages 1-32, May.
    10. Marzio Galeotti & Matteo Manera & Alessandro Lanza, 2006. "On the Robustness of Robustness Checks of the Environmental Kuznets Curve," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1027, Universitá degli Studi di Milano.
    11. Iacone, Fabrizio & Leybourne, Stephen J. & Robert Taylor, A.M., 2013. "Testing for a break in trend when the order of integration is unknown," Journal of Econometrics, Elsevier, vol. 176(1), pages 30-45.
    12. Uwe Hassler & Francesc Marmol & Carlos Velasco, 2008. "Fractional cointegration in the presence of linear trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(6), pages 1088-1103, November.
    13. 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.
    14. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2006. "Purchasing Power Parity: The Irish Experience Re-visited," Trinity Economics Papers tep200615, Trinity College Dublin, Department of Economics.

    More about this item

    Keywords

    Fractional cointegration; deterministic trends; ordinary least squares estimation; generalized least squares estimation; Wald tests.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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