Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівська модель векторної авторегресії (BVAR)× | Модель Фур'є VAR× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1984 | 2010s |
| Автор методу≠ | Doan, Litterman & Sims | Enders & Lee; extended by Nazlioglu and others to VAR systems |
| Тип | Multivariate time-series model | Multivariate time-series model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model | Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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