Publication Type
Journal Article
Version
publishedVersion
Publication Date
6-2008
Abstract
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 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, 971–1001). The approach also has useful extensions to dynamic panels.
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Theory
Volume
24
Issue
3
First Page
631
Last Page
650
ISSN
0266-4666
Identifier
10.1017/s0266466608080262
Publisher
Cambridge University Press
Citation
PHILLIPS, Peter C. B. and HAN, Chirok.
Gaussian Inference in AR(1) Time Series with or without a Unit Root. (2008). Econometric Theory. 24, (3), 631-650.
Available at: https://ink.library.smu.edu.sg/soe_research/249
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1017/s0266466608080262