Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| ARMA-model (Autoregressiv glidende gennemsnit)× | Strukturel Vektor Autoregression (SVAR)× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 1970 | 1980 |
| Ophavsperson≠ | George E. P. Box and Gwilym M. Jenkins | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Type≠ | Time series model | Multivariate time series model |
| Oprindelig kilde≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ |
| Aliasser | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Relaterede | 5 | 5 |
| Resumé≠ | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. | 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. |
| ScholarGateDatasæt ↗ |
|
|