Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Bayesiaans VAR-model (BVAR)× | Structurele Vector Autoregressie (SVAR)× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1984 | 1980 |
| Grondlegger≠ | Doan, Litterman & Sims | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Type≠ | Multivariate time-series model | Multivariate time series model |
| Oorspronkelijke bron≠ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ |
| Aliassen | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Verwant | 5 | 5 |
| Samenvatting≠ | The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large. | 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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