Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская векторная авторегрессия (BVAR)× | Модель векторной авторегрессии (VAR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1986 | 2005 |
| Автор метода≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Тип≠ | Bayesian multivariate time-series model | Multivariate time-series model |
| Основополагающий источник≠ | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Другие названия | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Связанные≠ | 5 | 4 |
| Сводка≠ | 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. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
| ScholarGateНабор данных ↗ |
|
|