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

Peter C.B. Phillips and Chirok Han

No 33500, Working Paper Series from Victoria University of Wellington, School of Economics and Finance

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)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:vuw:vuwecf:33500

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