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Unit‐root testing against the alternative hypothesis of up to m structural breaks
Abstract
Abstract. In this paper we provide tests for the unit‐root hypothesis against the occurrence of an unspecified number of breaks which may be larger than 2 but smaller that the maximum number of breaks allowed, m, in univariate time‐series models. The advocated procedure is considerably less computationally intensive than those widely used in the literature. We provide critical values for the test and examine its small sample properties through Monte Carlo experiments.
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Citations
Cites
Computation and analysis of multiple structural change models
Bai, J., & Perron, P. (2003) · Journal of Applied Econometrics · doi:10.1002/jae.659 · Cited by 5,771
Cited by
Boutabba, M. A. (2014) · Economic Modelling · doi:10.1016/j.econmod.2014.03.005 · Cited by 835
A new unit root test with two structural breaks in level and slope at unknown time
Narayan, P. K., & Popp, S. (2010) · Journal of Applied Statistics · doi:10.1080/02664760903039883 · Cited by 721
Carrion-i-Silvestre, J. L., Kim, D., & Perron, P. (2009) · Econometric Theory · doi:10.1017/s0266466609990326 · Cited by 474
Tests for cointegration allowing for an unknown number of breaks
Maki, D. (2012) · Economic Modelling · doi:10.1016/j.econmod.2012.04.022 · Cited by 444
Gunay, S., & Can, G. (2022) · PLOS ONE · doi:10.1371/journal.pone.0261835 · Cited by 92
Metadata
- Authors
- Kapetanios, G.
- Venue
- Journal of Time Series Analysis
- Publisher
- Wiley
- Published
- 2005-01
- Volume
- 26
- Issue
- 1
- Pages
- 123-133
- ISSN
- 0143-9782 (ISSN-L)
- 1467-9892
- OpenAlex
- W3124093763
- Cited by
- 265
- References
- 1
- FWCI
- 12.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kapetanios, G. (2005). Unit‐root testing against the alternative hypothesis of up to m structural breaks. Journal of Time Series Analysis. https://doi.org/10.1111/j.1467-9892.2005.00393.x
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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.
