Toward a unified interval estimation of autoregressions

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

27 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)705-717
Journal / PublicationEconometric Theory
Volume28
Issue number3
Publication statusPublished - Jun 2012
Externally publishedYes

Abstract

An empirical likelihood-based confidence interval is proposed for interval estimations of the autoregressive coefficient of a first-order autoregressive model via weighted score equations. Although the proposed weighted estimate is less efficient than the usual least squares estimate, its asymptotic limit is always normal without assuming stationarity of the process. Unlike the bootstrap method or the least squares procedure, the proposed empirical likelihood-based confidence interval is applicable regardless of whether the underlying autoregressive process is stationary, unit root, near-integrated, or even explosive, thereby providing a unified approach for interval estimation of an AR(1) model to encompass all situations. Finite-sample simulation studies confirm the effectiveness of the proposed method. © 2011 Cambridge University Press.

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Citation Format(s)

Toward a unified interval estimation of autoregressions. / Chan, Ngai Hang; Li, Deyuan; Peng, Liang.
In: Econometric Theory, Vol. 28, No. 3, 06.2012, p. 705-717.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review