Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vektora kļūdu labojuma modelis (VECM)× | Strukturālā vektorautoregresija (SVAR)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1987 | 1980 |
| Autors≠ | Robert F. Engle and Clive W. J. Granger | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Tips≠ | Multivariate time-series model | Multivariate time series model |
| Pirmavots≠ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ |
| Citi nosaukumi | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | 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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