Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| La laika mainīgo parametru VAR modelis (TVP-VAR)× | Strukturālā vektorautoregresija (SVAR)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2005 | 1980 |
| Autors≠ | Primiceri (2005); Cogley & Sargent (2001, 2005) | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Tips≠ | Multivariate time-series model with drifting coefficients | Multivariate time series model |
| Pirmavots≠ | Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852. 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 | TVP-VAR, time-varying VAR, TV-VAR, drifting-coefficient VAR | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | The Time-Varying Parameter VAR (TVP-VAR) model extends the standard vector autoregression by allowing the coefficients and error covariances to evolve gradually over time. Estimated via Bayesian methods and MCMC simulation, it captures how dynamic relationships between macroeconomic or financial variables shift across different economic regimes without requiring pre-specified break points. | 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. |
| ScholarGateDatu kopa ↗ |
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