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| Modello Vettoriale Autoregressivo Strutturale Non Lineare (NL-SVAR)× | Modello a Correzione d'Errore Vettoriale (VECM)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1990s–2010s | 1987 |
| Ideatore≠ | Extensions by Koop, Potter, Auerbach, Gorodnichenko and others | Robert F. Engle and Clive W. J. Granger |
| 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 ↗ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ |
| Alias | nonlinear structural VAR, NL-SVAR, threshold SVAR, regime-switching SVAR | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction 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. | The Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variable corrects back toward equilibrium after a shock, making it the standard tool for analysing cointegrated multivariate time series. |
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