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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2504.09646 |
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| _version_ | 1866910910709235712 |
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| author | Mamun, Tuhin G M Al |
| author_facet | Mamun, Tuhin G M Al |
| contents | This paper introduces the NSB-ARDL (Nonlinear Structural Break Autoregressive Distributed Lag) model, a novel econometric framework designed to capture asymmetric and nonlinear dynamics in macroeconomic time series. Traditional ARDL models, while widely used for estimating short- and long-run relationships, rely on assumptions of linearity and symmetry that may overlook critical structural features in real-world data. The NSB-ARDL model overcomes these limitations by decomposing explanatory variables into cumulative positive and negative partial sums, enabling the identification of both short- and long-term asymmetries.
Monte Carlo simulations show that NSB-ARDL consistently outperforms conventional ARDL models in terms of forecasting accuracy when asymmetric responses are present in the data-generating process. An empirical application to South Korea's CO2 emissions demonstrates the model's practical advantages, yielding a better in-sample fit and more interpretable long-run coefficients. These findings highlight the NSB-ARDL model as a structurally robust and forecasting-efficient alternative for analyzing nonlinear macroeconomic phenomena. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09646 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Replacing ARDL? Introducing the NSB-ARDL Model for Structural and Asymmetric Forecasting Mamun, Tuhin G M Al Methodology C22, C52, C53, Q54 This paper introduces the NSB-ARDL (Nonlinear Structural Break Autoregressive Distributed Lag) model, a novel econometric framework designed to capture asymmetric and nonlinear dynamics in macroeconomic time series. Traditional ARDL models, while widely used for estimating short- and long-run relationships, rely on assumptions of linearity and symmetry that may overlook critical structural features in real-world data. The NSB-ARDL model overcomes these limitations by decomposing explanatory variables into cumulative positive and negative partial sums, enabling the identification of both short- and long-term asymmetries. Monte Carlo simulations show that NSB-ARDL consistently outperforms conventional ARDL models in terms of forecasting accuracy when asymmetric responses are present in the data-generating process. An empirical application to South Korea's CO2 emissions demonstrates the model's practical advantages, yielding a better in-sample fit and more interpretable long-run coefficients. These findings highlight the NSB-ARDL model as a structurally robust and forecasting-efficient alternative for analyzing nonlinear macroeconomic phenomena. |
| title | Replacing ARDL? Introducing the NSB-ARDL Model for Structural and Asymmetric Forecasting |
| topic | Methodology C22, C52, C53, Q54 |
| url | https://arxiv.org/abs/2504.09646 |