Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Strukturālā vektorautoregresija (SVAR)× | Dinamiskais paneļa datu modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1980 | 1988–1991 |
| Autors≠ | Sims (1980); identification schemes by Blanchard & Quah (1989) | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Tips≠ | Multivariate time series model | Dynamic regression / GMM estimation |
| Pirmavots≠ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ | Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297. DOI ↗ |
| Citi nosaukumi | SVAR, structural vector autoregression, identified VAR, structural VAR model | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy. |
| ScholarGateDatu kopa ↗ |
|
|