Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель Байесовского векторного авторегрессионного анализа (BVAR)× | Модель Фурье-ВАР× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1984 | 2010s |
| Автор метода≠ | Doan, Litterman & Sims | Enders & Lee; extended by Nazlioglu and others to VAR systems |
| 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 ↗ | Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599. DOI ↗ |
| Другие названия | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model | Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR |
| Связанные≠ | 5 | 6 |
| 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. | The Fourier VAR model extends the standard Vector Autoregression by replacing fixed deterministic terms with Fourier trigonometric components, allowing the intercept (and optionally the trend) to shift gradually and smoothly over time. This eliminates the need to pre-specify the number, timing, or shape of structural breaks in a multivariate time-series system. |
| ScholarGateНабор данных ↗ |
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