GAUSSIAN INFERENCE IN AR(1) TIME SERIES WITH OR WITHOUT A UNIT ROOT

This paper introduces a simple first-difference-based approach to estimation and inference for the AR~1! model+ The estimates have virtually no finite-sample bias and are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform Mn rate of convergence+ En route, a useful central limit theorem ~CLT! for sample covariances of linear processes is given, following Phillips and Solo ~1992, Annals of Statistics, 20, 971–1001!+ The approach also has useful extensions to dynamic panels+