Published online by Cambridge University Press: 10 February 2004
The rank-based unit root tests proposed by Hasan and Koenker (1997, Econometrica 65, 133–161) have power equal to size for normal innovations. Unit root tests based on M-estimators exhibit the same behavior. The problem occurs because the test statistics are transformed to obtain computationally convenient critical values. I describe a convenient way to compute critical values without transforming the test statistics. The resulting tests are almost as powerful as least squares–based tests for normal errors and much more powerful for thicker tailed distributions.I thank Thomas Rothenberg for many useful comments. This paper is based on my dissertation, which he supervised. I also thank Jack Porter, Jim Powell, Richard Stanton, and Jim Stock for good advice. Comments of Bruce Hansen, the editor, and two anonymous referees improved the exposition.