Estimating Trending Variables In The Presence Of Fractionally Integrated Errors
AbstractThis 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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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 16 (2000)
Issue (Month): 03 (June)
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- Joerg Breitung and Uwe Hassler, 2001.
"Inference on the Cointegration Rank in Fractionally Integrated Processes,"
Computing in Economics and Finance 2001
233, Society for Computational Economics.
- 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.
- Breitung, Jörg & Hassler, Uwe, 2000. "Inference on the cointegration rank in fractionally integrated processes," SFB 373 Discussion Papers 2000,65, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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