When testing for a change in mean of a time series, the null hypothesis is no change in mean. However, a change in mean causes a bias in the estimation of serial correlation parameters. This bias can cause nonmonotonic power to the point that if the change is big enough, power can go to zero. In...

We develop unit root tests using additional stationary covariates as suggested in Hansen (1995). However, we allow for the covariates to enter the model in a nonparametric fashion, so that our model is an extension of the semiparametric model analyzed in Robinson (1988). We retain a linear...

When testing for a change in mean of a time series, the null hypothesis is no change in mean. However, a change in mean causes a bias in the estimation of serial correlation parameters. This bias can cause nonmonotonic power to the point that if the change is big enough, power can go to zero. In...

Several widely used tests for a changing mean exhibit nonmonotonic power in finite samples, due to "incorrect" estimation of nuisance parameters under the alternative. In this paper, we study the issue of nonmonotonic power in testing for changing mean. We investigate the asymptotic power...

Several widely used tests for a changing mean exhibit nonmonotonic power in ¯nite samples due to \incorrect" estimation of nuisance parameters under the alternative. In this paper, we study the issue of nonmonotonic power in testing for changing mean. We investigate the asymptotic power...