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An I(d) Model with Trend and Cycles

Author

Listed:
  • Karim M. Abadir

    (Imperial College Business School, Imperial College London, London, UK)

  • Walter Distaso

    (Imperial College Business School, Imperial College London, London, UK)

  • Liudas Giraitis

    (Department of Economics, Queen Mary, University of London, London, UK)

Abstract

This paper deals with models allowing for trending processes and cyclical component with error processes that are possibly nonstationary, nonlinear, and non-Gaussian. Asymptotic confidence intervals for the trend, cyclical component, and memory parameters are obtained. The confidence intervals are applicable for a wide class of processes, exhibit good coverage accuracy, and are easy to implement.

Suggested Citation

  • Karim M. Abadir & Walter Distaso & Liudas Giraitis, 2010. "An I(d) Model with Trend and Cycles," Working Paper series 18_10, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:18_10
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    File URL: http://www.rcea.org/RePEc/pdf/wp18_10.pdf
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    Cited by:

    1. is not listed on IDEAS
    2. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    3. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2024. "Partially one-sided semiparametric inference for trending persistent and antipersistent processes," Econometrics and Statistics, Elsevier, vol. 30(C), pages 1-14.
    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. Bailey, Natalia & Giraitis, Liudas, 2016. "Spectral approach to parameter-free unit root testing," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 4-16.
    6. Papailias, Fotis & Fruet Dias, Gustavo, 2015. "Forecasting long memory series subject to structural change: A two-stage approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1056-1066.
    7. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.
    8. 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.
    9. Ying Lun Cheung & Uwe Hassler, 2020. "Whittle-type estimation under long memory and nonstationarity," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(3), pages 363-383, September.

    More about this item

    Keywords

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    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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