The paper questions the appropriateness of the practice known as 'error-autocorrelation correcting' in linear regression, by showing that adopting an AR(1) error formulation is equivalent to assuming that the regressand does not Granger cause any of the regressors. This result is used to construct a new test for the common factor restrictions, as well as investigate - using Monte Carlo simulations - other potential sources of unreliability of inference resulting from this practice. The main conclusion is that when the Granger cause restriction is false, the ordinary least square and generalized least square estimators are biased and inconsistent, and using autocorrelation-consistent standard errors does not improve the reliability of inference. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2009.
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