Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2013
Abstract
The paper proposes a novel inference procedure for long-horizon predictive regression with persistent regressors, allowing the autoregressive roots to lie in a wide vicinity of unity. The invalidity of conventional tests when regressors are persistent has led to a large literature dealing with inference in predictive regressions with local to unity regressors. Magdalinos and Phillips (2009b) recently developed a new framework of extended IV procedures (IVX) that enables robtist chi-square testing for a wider class of persistent regressors. We extend this robust procedure to an even wider parameter space in the vicinity of unity and apply the methods to long-horizon predictive regression. Existing methods in this model, which rely on simulated critical values by inverting tests under local to unity conditions, cannot be easily extended beyond the scalar regressor case or to wider autoregressive parametrizations. In contrast, the methods developed here lead to standard chi-square tests, allow for multivariate regressors, and include predictive processes whose roots may lie in a wide vicinity of unity. As such they have many potential applications in predictive regression. In addition to asymptotics under the null hypothesis of no predictability, the paper investigates validity under the alternative, showing how balance in the regression may be achieved through the use of localizing coefficients and developing local asymptotic power properties under such alternatives. These results help to explain some of the empirical difficulties that have been encountered in establishing predictability of stock returns. (C) 2013 Elsevier B.V. All rights reserved.
Keywords
Asymptotic theory, Balanced regression, Endogeneity, Instrumentation, IVX methods, Local power, Mild integration, Mildly explosive, Predictive regression, Robustness
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
177
Issue
2
First Page
250
Last Page
264
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2013.04.011
Publisher
Elsevier
Citation
Peter C. B. PHILLIPS and LEE, Ji Hyung.
Predictive Regression under Various Degrees of Persistence and Robust Long-Horizon Regression. (2013). Journal of Econometrics. 177, (2), 250-264.
Available at: https://ink.library.smu.edu.sg/soe_research/1828
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/10.1016/j.jeconom.2013.04.011