Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель Байесовского скользящего среднего (MA)× | Модель Байесовского векторного авторегрессионного анализа (BVAR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1970s–1997 | 1984 |
| Автор метода≠ | Bayesian framework applied to Box-Jenkins MA models; West & Harrison (1997) canonical treatment | Doan, Litterman & Sims |
| Тип≠ | Bayesian time series model | Multivariate time-series model |
| Основополагающий источник≠ | West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ |
| Другие названия | Bayesian MA, Bayesian moving average, BMA time series, MA model with Bayesian estimation | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Связанные≠ | 6 | 5 |
| Сводка≠ | The Bayesian MA model estimates a moving average time series model within a fully Bayesian framework, placing prior distributions on the MA parameters and error variance and updating them via Bayes' theorem. This approach yields full posterior distributions over model parameters and produces probabilistic forecasts with coherent uncertainty quantification. | 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. |
| ScholarGateНабор данных ↗ |
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