Publication Type
Journal Article
Version
submittedVersion
Publication Date
11-2015
Abstract
This article provides the limit theory of real-time dating algorithms for bubble detection that were suggested in Phillips, Wu, and Yu (PWY; International Economic Review 52 [2011], 201-26) and in a companion paper by the present authors (Phillips, Shi, and Yu, 2015; PSY; International Economic Review 56 [2015a], 1099-1134. Bubbles are modeled using mildly explosive bubble episodes that are embedded within longer periods where the data evolve as a stochastic trend, thereby capturing normal market behavior as well as exuberance and collapse. Both the PWY and PSY estimates rely on recursive right-tailed unit root tests (each with a different recursive algorithm) that may be used in real time to locate the origination and collapse dates of bubbles. Under certain explicit conditions, the moving window detector of PSY is shown to be a consistent dating algorithm even in the presence of multiple bubbles. The other algorithms are consistent detectors for bubbles early in the sample and, under stronger conditions, for subsequent bubbles in some cases. These asymptotic results and accompanying simulations guide the practical implementation of the procedures. They indicate that the PSY moving window detector is more reliable than the PWY strategy, sequential application of the PWY procedure, and the CUSUM procedure.
Keywords
Bubble duration, Consistency, Dating algorithm, Limit theory, Multiple bubbles, Real time detector
Discipline
Econometrics | Finance and Financial Management
Research Areas
Econometrics
Publication
International Economic Review
Volume
56
Issue
4
First Page
1079
Last Page
1134
ISSN
0020-6598
Identifier
10.1111/iere.12131
Publisher
Wiley
Citation
Peter C. B. PHILLIPS; SHI, Shuping; and Jun YU.
Testing for multiple bubbles: Limit theory of real-time detectors. (2015). International Economic Review. 56, (4), 1079-1134.
Available at: https://ink.library.smu.edu.sg/soe_research/1794
Copyright Owner and License
Authors
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/10.1111/iere.12131