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| Modello B-SVAR (Bayesian Structural VAR)× | Modello a Correzione d'Errore Vettoriale (VECM)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1998–2005 | 1987 |
| Ideatore≠ | Sims & Zha (1998); Uhlig (2005) for sign-restriction identification | Robert F. Engle and Clive W. J. Granger |
| Tipo≠ | Structural multivariate time-series model | Multivariate time-series model |
| Fonte seminale≠ | Sims, C. A., & Zha, T. (1998). Bayesian methods for dynamic multivariate models. International Economic Review, 39(4), 949–968. 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 | Bayesian SVAR, B-SVAR, Bayesian structural VAR, Bayesian identified VAR | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | The Bayesian Structural Vector Autoregression model combines the structural identification of SVAR with Bayesian prior distributions over parameters. It estimates causal impulse responses between multiple time series while incorporating prior economic knowledge and producing full posterior uncertainty bands rather than point estimates alone. | 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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