Some properties of exact tests for unit roots

This paper is concerned with the null hypothesis that errors in a regression equation for time series data follow a random walk. We examine the power properties of most powerful invariant tests for the unit root null hypotheses against exact stationary and nonstationary first order autoregressive models. The analysis shows the importance of a constant term and a linear trend variable in certain cases. The implications of the results for models estimated using seasonal data are briefly discussed.