Buffered vector error-correction models: an application to the U.S. Treasury bond rates
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Abstract
This paper extends the buffered autoregressive model to the buffered vector error-correction model (VECM). Least squares estimation and a reduced-rank estimation are discussed, and the consistency of the estimators on the delay parameter and threshold parameters is derived. We also propose a supWald test for the presence of buffer-type threshold effect. Under the null hypothesis of no threshold, the supWald test statistic converges to a function of Gaussian process. A bootstrap method is proposed to obtain the p-value for the supWald test. We investigate the effectiveness of our methods by simulation studies. We apply our model to study the monthly Federal bond rates of United States. We find the evidences of buffering regimes and the asymmetric error-correction effect.
Funding source: Research Grants Council of the Hong Kong Special Administrative Region, China
Award Identifier / Grant number: T31-604/18-N
Award Identifier / Grant number: 17304417
Acknowledgments
The authors would like to thank the reviewer for the valuable and constructive comments which improved the paper. The research of Philip L.H. Yu was partially supported by a Theme-based Research Fund and a General Research Fund from the Research Grants Council of the Hong Kong Special Administrative Region, China (Projects T31-604/18-N and 17304417).
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: The research of Philip L.H. Yu was partially supported by a Theme-based Research Fund and a General Research Fund from the Research Grants Council of the Hong Kong Special Administrative Region, China (Projects T31-604/18-N and 17304417).
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Supplementary material
The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2019-0047).
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Articles in the same Issue
- Frontmatter
- Research Articles
- Recovering cointegration via wavelets in the presence of non-linear patterns
- Buffered vector error-correction models: an application to the U.S. Treasury bond rates
- Long-memory modeling and forecasting: evidence from the U.S. historical series of inflation
- Modeling time-varying parameters using artificial neural networks: a GARCH illustration
- Variable elasticity of substitution and economic growth in the neoclassical model
- Fiscal austerity in emerging market economies
- Selecting between causal and noncausal models with quantile autoregressions
Articles in the same Issue
- Frontmatter
- Research Articles
- Recovering cointegration via wavelets in the presence of non-linear patterns
- Buffered vector error-correction models: an application to the U.S. Treasury bond rates
- Long-memory modeling and forecasting: evidence from the U.S. historical series of inflation
- Modeling time-varying parameters using artificial neural networks: a GARCH illustration
- Variable elasticity of substitution and economic growth in the neoclassical model
- Fiscal austerity in emerging market economies
- Selecting between causal and noncausal models with quantile autoregressions
