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راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| نموذج الانحدار الذاتي المتجه الهيكلي غير الخطي (NL-SVAR)× | نموذج الانحدار الذاتي غير الخطي (NARDL)× | |
|---|---|---|
| المجال | الاقتصاد القياسي | الاقتصاد القياسي |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 1990s–2010s | 2014 |
| صاحب الطريقة≠ | Extensions by Koop, Potter, Auerbach, Gorodnichenko and others | Shin, Yu & Greenwood-Nimmo |
| النوع≠ | Multivariate nonlinear structural time series model | Nonlinear cointegration model |
| المصدر التأسيسي≠ | Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4), 267–358. DOI ↗ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer. link ↗ |
| الأسماء البديلة | nonlinear structural VAR, NL-SVAR, threshold SVAR, regime-switching SVAR | NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model |
| ذات صلة≠ | 6 | 5 |
| الملخص≠ | The Nonlinear Structural VAR model extends the standard SVAR framework to allow structural relationships and dynamic responses to vary across economic regimes or states of the world. By imposing nonlinear transition mechanisms — such as threshold switching or smooth regime change — it captures asymmetric responses to shocks that a linear SVAR cannot detect. | The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically. |
| ScholarGateمجموعة البيانات ↗ |
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