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| Байесов модел на авторегресия (AR)× | Байесов модел на векторна авторегресия (BVAR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1971 | 1984 |
| Създател≠ | Arnold Zellner; foundational Bayesian time-series work by West & Harrison | Doan, Litterman & Sims |
| Тип≠ | Bayesian time-series model | Multivariate time-series model |
| Основополагащ източник≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ |
| Други названия | Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Свързани≠ | 6 | 5 |
| Резюме≠ | The Bayesian AR model estimates an autoregressive time-series process by combining a likelihood derived from the AR structure with prior distributions over the lag coefficients and error variance. Rather than producing single point estimates, it yields full posterior distributions, enabling principled uncertainty quantification and probabilistic forecasting. | 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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