TOWARD A UNIFIED INTERVAL ESTIMATION OF AUTOREGRESSIONS
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.
Year of publication: |
2012
|
---|---|
Authors: | Chan, Ngai Hang ; Li, Deyuan ; Peng, Liang |
Published in: |
Econometric Theory. - Cambridge University Press. - Vol. 28.2012, 03, p. 705-717
|
Publisher: |
Cambridge University Press |
Description of contents: | Abstract [journals.cambridge.org] |
Saved in:
Saved in favorites
Similar items by person
-
Toward a unified interval estimation of autoregressions
Chan, Ngai Hang, (2012)
-
Tail index of an AR(1) model with ARCH(1) errors
Chan, Ngai Hang, (2013)
-
TOWARD A UNIFIED INTERVAL ESTIMATION OF AUTOREGRESSIONS
Chan, Ngai Hang, (2011)
- More ...