Publication Type

Journal Article

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1017/s0266466608080262

Included in

Econometrics Commons

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