  The behavior of the power function of autocorrelation tests such as the
Durbin-Watson test in time series regressions or the Cliff-Ord test in spatial
regression models has been intensively studied in the literature. When the
correlation becomes strong, Kr\"amer (1985) (for the Durbin-Watson test) and
Kr\"amer (2005) (for the Cliff-Ord test) have shown that the power can be very
low, in fact can converge to zero, under certain circumstances. Motivated by
these results, Martellosio (2010) set out to build a general theory that would
explain these findings. Unfortunately, Martellosio (2010) does not achieve this
goal, as a substantial portion of his results and proofs suffer from serious
flaws. The present paper now builds a theory as envisioned in Martellosio
(2010) in a fairly general framework, covering general invariant tests of a
hypothesis on the disturbance covariance matrix in a linear regression model.
The general results are then specialized to testing for spatial correlation and
to autocorrelation testing in time series regression models. We also
characterize the situation where the null and the alternative hypothesis are
indistinguishable by invariant tests.
