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A Comparison of Unit-Root Test Criteria
Open accessAbstract
During the past 15 years, the ordinary least squares estimator and the corresponding pivotal statistic have been widely used for testing the unit-root hypothesis in autoregressive processes. Recently, several new criteria, based on maximum likelihood estimators and weighted symmetric estimators, have been proposed. In this article, we describe several different test criteria. Results from a Monte Carlo study that compares the power of the different criteria indicate that the new tests are more powerful against the stationary alternative. Of the procedures studied, the weighted symmetric estimator and the unconditional maximum likelihood estimator provide the most powerful tests against the stationary alternative. As an illustration, the weekly series of one-month treasury-bill rates is analyzed.
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Citations
Cited by
A simple panel unit root test in the presence of cross‐section dependence
Pesaran, M. H. (2007) · Journal of Applied Econometrics · doi:10.1002/jae.951 · Cited by 12,787
A pair-wise approach to testing for output and growth convergence
Hashem Pesaran, M. (2007) · Journal of Econometrics · doi:10.1016/j.jeconom.2006.05.024 · Cited by 343
Metadata
- Authors
- Pantula, S. G., Gonzalez-Farias, G., & Fuller, W. A.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 1994-10
- Volume
- 12
- Issue
- 4
- Pages
- 449-459
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W1989188582
- Cited by
- 447
- References
- 0
- FWCI
- 21.86
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: A Comparison of Unit-Root Test Criteria. https://app.cochat.ai/papers/10.1080/07350015.1994.10524567
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- Pantula, S. G., Gonzalez-Farias, G., & Fuller, W. A. (1994). A Comparison of Unit-Root Test Criteria. Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.1994.10524567
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
