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Estimating Trending Variables In The Presence Of Fractionally Integrated Errors

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  • Tsay, Wen-Jen

Abstract

This paper considers the problems of estimation and inference in the linear regression model with fractionally integrated errors. The ordinary least squares (OLS) and the first differenced (FD) estimators are studied. Relative to the OLS estimators, a substantial increase in the convergence rates of the coefficient estimator for the stochastic regressor can be achieved by the FD estimators when the error term is nonstationary. However, the preceding decisive results can not always sustain when the error term is stationary. We also find that the FD estimators can eliminate the spurious regression because the FD t-ratio for the coefficient estimators never diverges.

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  • Tsay, Wen-Jen, 2000. "Estimating Trending Variables In The Presence Of Fractionally Integrated Errors," Econometric Theory, Cambridge University Press, vol. 16(3), pages 324-346, June.
    • Handle: RePEc:cup:etheor:v:16:y:2000:i:03:p:324-346_16
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    Cited by:

    1. Breitung, Jorg & Hassler, Uwe, 2002. "Inference on the cointegration rank in fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 110(2), pages 167-185, October.
    2. Wang Shin-Huei & Hafner Christian, 2011. "Estimating Autocorrelations in the Presence of Deterministic Trends," Journal of Time Series Econometrics, De Gruyter, vol. 3(2), pages 1-25, April.
    3. 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.

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