Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-1986
Abstract
This paper provides an analytical study of linear regressions involving the levels of economic time series. An asymptotic theory is developed for regressions that relate quite general integrated random processes. This includes the spurious regressions of Granger and Newbold (1974) and the recent cointegrating regressions of Granger and Engle (1985). An asymptotic theory is developed for the regression coefficients and for conventional significance tests. It is shown that the usual t- and F-ratio test statistics do not possess limiting distributions in this context but actually diverge as the sample size T ↑ ∞. The limiting behavior of regression diagnostics such as the Durbin–Watson statistic, the coefficient of determination and the Box–Pierce statistic is also analyzed. The theoretical results that we present explain many of the earlier simulation findings of Granger and Newbold, 1974, Granger and Newbold, 1977.
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
33
Issue
3
First Page
311
Last Page
340
ISSN
0304-4076
Identifier
10.1016/0304-4076(86)90001-1
Publisher
Elsevier
Citation
PHILLIPS, Peter C. B..
Understanding spurious regressions in econometrics. (1986). Journal of Econometrics. 33, (3), 311-340.
Available at: https://ink.library.smu.edu.sg/soe_research/2814
Copyright Owner and License
Publisher
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/10.1016/0304-4076(86)90001-1