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Finite-Sample Power of the Durbin-Watson Test Against Fractionally Integrated Disturbances

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  • Prof. Dr. Walter Krämer

    ()
    (Faculty of Statistics, Dortmund University of Technology)

  • Christian Kleiber

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

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Paper provided by Business and Social Statistics Department, Technische Universität Dortmund in its series Working Papers with number 10.

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Publication status: Published in The Econometrics Journal 8, 2005, pages 406-417
Handle: RePEc:dor:wpaper:10

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Cited by:
  1. Martellosio, Federico, 2008. "Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression," MPRA Paper 7255, University Library of Munich, Germany.
  2. Anurag Banerjee, 2004. "Sensitivity of OLS estimates against ARFIMA error process as small sample Test for long memory," Econometric Society 2004 Australasian Meetings 159, Econometric Society.

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