Publication Date: January 2006
This note introduces a simple ﬁrst-diﬀerence-based approach to estimation and inference for the AR(1) model. The estimates have virtually no ﬁnite sample bias, 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 /n rate of convergence. En route, a useful CLT for sample covariances of linear processes is given, following Phillips and Solo (1992). The approach also has useful extensions to dynamic panels.
Autoregression, Diﬀerencing, Gaussian limit, Mildly explosive processes, Uniformity, Unit root
JEL Classification Codes: C22
See CFP: 1243