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| Modello Bayesiano a Correzione d'Errore Vettoriale (Bayesian VECM)× | Modello a Correzione d'Errore Vettoriale (VECM)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2002–2005 | 1987 |
| Ideatore≠ | Kleibergen & Paap; Villani | Robert F. Engle and Clive W. J. Granger |
| Tipo≠ | Bayesian multivariate time series model | Multivariate time-series model |
| Fonte seminale≠ | Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249. 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 VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model |
| Correlati | 5 | 5 |
| Sintesi≠ | The Bayesian VECM combines the classical Vector Error Correction Model — which captures both short-run dynamics and long-run cointegrating relationships among non-stationary multivariate time series — with Bayesian prior distributions over the cointegrating rank and coefficient matrices. This allows principled uncertainty quantification, incorporation of economic theory as priors, and coherent inference even in small samples. | 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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