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Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| АРСС модел (авторегресионна плъзгаща се средна)× | Структурна векторна авторегресия (SVAR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1970 | 1980 |
| Създател≠ | George E. P. Box and Gwilym M. Jenkins | Sims (1980); identification schemes by Blanchard & Quah (1989) |
| Тип≠ | Time series model | Multivariate time series model |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | SVAR, structural vector autoregression, identified VAR, structural VAR model |
| Свързани | 5 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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