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| Modello Vettoriale Autoregressivo Strutturale Non Lineare (NL-SVAR)× | Structural Vector Autoregression (SVAR)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1990s–2010s | 1980 |
| Ideatore≠ | Extensions by Koop, Potter, Auerbach, Gorodnichenko and others | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Tipo≠ | Multivariate nonlinear structural time series model | Multivariate time series model |
| Fonte seminale≠ | Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4), 267–358. DOI ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ |
| Alias | nonlinear structural VAR, NL-SVAR, threshold SVAR, regime-switching SVAR | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | 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. | Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions. |
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