Gaussian inference in AR(1) time series with or without a unit root

Peter C.B. Phillips, Chirok Han

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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 $\sqrt{n}$ 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, 9711001). The approach also has useful extensions to dynamic panels.

Original languageEnglish
Pages (from-to)631-650
Number of pages20
JournalEconometric Theory
Issue number3
Publication statusPublished - 2008 Jun
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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