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

Peter C. B. Phillips () and Chirok Han ()

No 1546, Cowles Foundation Discussion Papers from Cowles Foundation, 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)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: Written
Note: CFP 1243
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Published in Econometric Theory (June 2008), 24(3): 631-650

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Journal Article: GAUSSIAN INFERENCE IN AR(1) TIME SERIES WITH OR WITHOUT A UNIT ROOT (2008)
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