Publication Type

Journal Article

Version

Preprint

Publication Date

5-2014

Abstract

Local to unity limit theory is used in applications to construct confidence intervals (CIs) for autoregressive roots through inversion of a unit root test (Stock (1991)). Such CIs are asymptotically valid when the true model has an autoregressive root that is local to unity (rho = 1 + c/n), but are shown here to be invalid at the limits of the domain of definition of the localizing coefficient c because of a failure in tightness and the escape of probability mass. Failure at the boundary implies that these CIs have zero asymptotic coverage probability in the stationary case and vicinities of unity that are wider than O(n(-1/3)). The inversion methods of Hansen (1999) and Mikusheva (2007) are asymptotically valid in such cases. Implications of these results for predictive regression tests are explored. When the predictive regressor is stationary, the popular Campbell and Yogo (2006) CIs for the regression coefficient have zero coverage probability asymptotically, and their predictive test statistic Q erroneously indicates predictability with probability approaching unity when the null of no predictability holds. These results have obvious cautionary implications for the use of the procedures in empirical practice.

Keywords

Autoregressive root, Confidence belt, Confidence interval, Coverage probability, Local to unity, Localizing coefficient, Predictive regression, Tightness

Discipline

Econometrics

Research Areas

Econometrics

Publication

Econometrica

Volume

82

Issue

3

First Page

1177

Last Page

1195

ISSN

0012-9682

Identifier

10.3982/ECTA11094

Publisher

Econometric Society: Econometrica

Copyright Owner and License

Author

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

http://doi.org/10.3982/ECTA11094

Included in

Econometrics Commons

Share

COinS