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| Structural Break NARDL× | Test de cointegració d'Engle-Granger× | |
|---|---|---|
| Camp | Econometria | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 2014–2018 | 1987 |
| Autor original≠ | Shin, Yu & Greenwood-Nimmo (NARDL base); structural break extensions by subsequent applied researchers | Robert F. Engle and Clive W. J. Granger |
| Tipus≠ | Nonlinear cointegration with structural breaks | Cointegration test |
| Font seminal≠ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer. DOI ↗ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ |
| Àlies | SB-NARDL, NARDL with structural breaks, nonlinear ARDL with break, asymmetric ARDL structural break | EG cointegration test, Engle-Granger two-step method, residual-based cointegration test, EG test |
| Relacionats≠ | 6 | 5 |
| Resum≠ | Structural Break NARDL extends the Nonlinear Autoregressive Distributed Lag (NARDL) bounds-testing framework by explicitly accommodating one or more structural breaks in the long-run relationship. It separates positive and negative changes in the regressor, tests for cointegration, and allows regime shifts, providing a richer picture of asymmetric and break-sensitive dynamics between variables. | The Engle-Granger two-step method tests whether two or more non-stationary I(1) time series share a common stochastic trend — that is, whether a linear combination of them is stationary. If cointegration is confirmed, an error-correction model (ECM) can be estimated to capture both short-run dynamics and long-run equilibrium adjustment. |
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