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Band spectrum regression for cointegrated time series with long memory innovations

Author

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  • Marinucci, D.

Abstract

Band spectrum regression is considered for cointegrated time series with long memory innovations. The estimates we advocate are shown to be consistent when cointegrating relationships among stationary variables are investigated, while OLS are inconsistent due to correlation between the regressor and the cointegrating residuals; in the presence of unit roots, these estimates share the same asymptotic distribution as OLS. As a corollary of the main result, we provide a functional central limit theorem for quadratic forms in nonstationary fractionally integrated processes.

Suggested Citation

  • Marinucci, D., 1998. "Band spectrum regression for cointegrated time series with long memory innovations," LSE Research Online Documents on Economics 6871, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:6871
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    File URL: http://eprints.lse.ac.uk/6871/
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    Cited by:

    1. Francesc Marmol & Juan J. Dolado, 1999. "Asymptotic Inference for Nonstationary Fractionally Integrated Processes," Computing in Economics and Finance 1999 513, Society for Computational Economics.
    2. M. Azimmohseni & M. Khalafi & M. Kordkatuli, 2019. "Time series analysis of covariance based on linear transfer function models," Statistical Inference for Stochastic Processes, Springer, vol. 22(1), pages 1-16, April.

    More about this item

    Keywords

    Long-range dependence; band spectrum regression; cointegration;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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