Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель Байесовского векторного авторегрессионного анализа (BVAR)× | Векторная авторегрессия (VAR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1984 | 1980 |
| Автор метода≠ | Doan, Litterman & Sims | Christopher A. Sims |
| Тип | Multivariate time-series model | Multivariate time-series model |
| Основополагающий источник≠ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| Другие названия | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| Связанные | 5 | 5 |
| Сводка≠ | The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large. | Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance. |
| ScholarGateНабор данных ↗ |
|
|