Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Байесов векторна авторегресия (BVAR)× | Структурен модел на времеви редове (Основен структурен модел)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1986 | 1990 |
| Създател≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Andrew C. Harvey |
| Тип≠ | Bayesian multivariate time-series model | State-space (unobserved components) time series model |
| Основополагащ източник≠ | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Други названия | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Свързани≠ | 5 | 4 |
| Резюме≠ | Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts. | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. |
| ScholarGateНабор от данни ↗ |
|
|