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| Байесов модел на векторна авторегресия (BVAR)× | Модел на Фуриеров векторна авторегресия (VAR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1984 | 2010s |
| Създател≠ | Doan, Litterman & Sims | Enders & Lee; extended by Nazlioglu and others to VAR systems |
| Тип | Multivariate time-series model | Multivariate time-series model |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model | Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR |
| Свързани≠ | 5 | 6 |
| Резюме≠ | 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 от данни ↗ |
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