INFERENCE IN TIME-SERIES REGRESSION WHEN THE ORDER OF INTEGRATION OF A REGRESSOR IS UNKNOWN

The distribution of statistics testing restrictions on the coefficients in time series regressions can depend on the order of integration of the regressors. In practice, the order of integration is rarely known. We examine two conventional approaches to this problem - simply to ignore unit root problems or to use unit root pretests to determine the critical values for second-stage inference-and show that both exhibit substantial size distortions in empirically plausible situations. We then propose an alternative approach in which the second-stage critical values depend continuously on a first-stage statistic that is informative about the order of integration of the regressor. This procedure has the correct size asymptotically and good local asymptotic power.