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| Модел с времево променящи се параметри (TVP-VAR)× | Байесов модел на векторна авторегресия (BVAR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2005 | 1984 |
| Създател≠ | Primiceri (2005); Cogley & Sargent (2001, 2005) | Doan, Litterman & Sims |
| Тип≠ | Multivariate time-series model with drifting coefficients | Multivariate time-series model |
| Основополагащ източник≠ | Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852. DOI ↗ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ |
| Други названия | TVP-VAR, time-varying VAR, TV-VAR, drifting-coefficient VAR | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Свързани≠ | 6 | 5 |
| Резюме≠ | The Time-Varying Parameter VAR (TVP-VAR) model extends the standard vector autoregression by allowing the coefficients and error covariances to evolve gradually over time. Estimated via Bayesian methods and MCMC simulation, it captures how dynamic relationships between macroeconomic or financial variables shift across different economic regimes without requiring pre-specified break points. | 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 от данни ↗ |
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