Gaussian Inference in AR(1) Time Series with or without a Unit Root

Peter Phillips and Chirok Han

No 1546, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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 vn 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.

Keywords: Autoregression; Differencing; Gaussian limit; Mildly explosive processes; Uniformity; Unit root (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2006-01
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: CFP 1243
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Econometric Theory (June 2008), 24(3): 631-650

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