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Error‐correction Mechanism Tests for Cointegration in a Single‐equation Framework
Open accessAbstract
A new test is proposed for cointegration in a single‐equation framework where the regressors are weakly exogenous for the parameters of interest. The test is denoted as an error‐correction mechanism (ECM) test and is based upon the ordinary least squares coefficient of the lagged dependent variable in an autoregressive distributed lag model augmented with leads of the regressors. The limit distributions of the standardized coeffi cient and t‐ratio versions of the ECM tests are obtained and critical values are provided. These limit distributions do not depend upon nuisance parameters but they depend on the number of regressors. Finally, we compare their power properties with those of other cointegration tests available in the literature and find the circumstances under which the ECM tests have a better performance.
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Cites
Co-integration and error correction: representation, estimation and testing
Engle, R. F., & Granger, C. W. J. (1987) · Econometrica · doi:10.2307/1913236 · Cited by 32,007
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Westerlund, J. (2007) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.2007.00477.x · Cited by 6,113
Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework
Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014) · Festschrift in Honor of Peter Schmidt · doi:10.1007/978-1-4899-8008-3_9 · Cited by 3,835
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Metadata
- Authors
- Banerjee, A., Dolado, J., & Mestre, R.
- Venue
- Journal of Time Series Analysis
- Publisher
- Wiley
- Published
- 1998-05
- Volume
- 19
- Issue
- 3
- Pages
- 267-283
- ISSN
- 0143-9782 (ISSN-L)
- 1467-9892
- OpenAlex
- W1994045802
- Cited by
- 2,305
- References
- 1
- FWCI
- 24.84
- Type
- journal article
- Language
- en
- Open access
- green
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- Banerjee, A., Dolado, J., & Mestre, R. (1998). Error‐correction Mechanism Tests for Cointegration in a Single‐equation Framework. Journal of Time Series Analysis. https://doi.org/10.1111/1467-9892.00091
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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.