Abstract :
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+
