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Finite-sample power of the Durbin--Watson test against fractionally integrated disturbances

Christian Kleiber and Walter Krämer ()

Econometrics Journal, 2005, vol. 8, issue 3, 406-417

Abstract: We consider the finite-sample power of various tests against serial correlation in the disturbances of a linear regression model when these disturbances follow certain stationary long-memory processes. It emerges that the power depends on the form of the regressor matrix and that, for the Durbin--Watson test and many other tests that can be written as ratios of quadratic forms in the disturbances, the power can drop to zero as the long-memory parameter approaches the boundary of the stationarity region. The problem does not arise when the regression includes an intercept. We also provide a means to detect this zero-power trap for given regressors. Our analytical results are illustrated using fractionally integrated white noise and ARFIMA(1, d, 0) disturbances with artificial regressors and with a real data set. Copyright 2005 Royal Economic Society

Date: 2005
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Working Paper: Finite-Sample Power of the Durbin-Watson Test Against Fractionally Integrated Disturbances
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Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms

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