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TOWARD A UNIFIED INTERVAL ESTIMATION OFAUTOREGRESSIONS

Published online by Cambridge University Press:  25 November 2011

Ngai Hang Chan,
Deyuan Li and
Liang Peng
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Abstract

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An empirical likelihood–based confidence interval isproposed for interval estimations of theautoregressive coefficient of a first-orderautoregressive model via weighted score equations.Although the proposed weighted estimate is lessefficient than the usual least squares estimate, itsasymptotic limit is always normal without assumingstationarity of the process. Unlike the bootstrapmethod or the least squares procedure, the proposedempirical likelihood–based confidence interval isapplicable regardless of whether the underlyingautoregressive process is stationary, unit root,near-integrated, or even explosive, therebyproviding a unified approach for interval estimationof an AR(1) model to encompass all situations.Finite-sample simulation studies confirm theeffectiveness of the proposed method.

Information

Type
Brief Report
Information
Econometric Theory , Volume 28 , Issue 3 , June 2012, pp. 705 - 717
Copyright
Copyright © Cambridge University Press 2011

Footnotes

We thank three anonymous referees, the editor,and the co-editor Giuseppe Cavaliere for helpfulreferences and constructive suggestions, which ledto an improved version of this note. This researchwas supported in part by grants from HKSAR-RGC-GRFnos. 400306, 400308, and 400410, NSA grant no.H98230-10-1-0170, NSF grant no. DMS1005336, andNNSFC grant no. 10801038.

References

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