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| Bayesiläinen VECM (Bayesian VECM)× | Rakenteellinen vektoritodennäköisyysautoregressio (SVAR)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2002–2005 | 1980 |
| Kehittäjä≠ | Kleibergen & Paap; Villani | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Tyyppi≠ | Bayesian multivariate time series model | Multivariate time series model |
| Alkuperäislähde≠ | Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249. DOI ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ |
| Rinnakkaisnimet | Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | 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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