Regression model
贝叶斯向量自回归 (BVAR)
贝叶斯向量自回归 (BVAR) 在向量自回归模型中加入明尼苏达或其他先验分布，以控制模型参数过度化问题。该方法由 Litterman (1986) 提出，并由 Bańbura, Giannone 和 Reichlin (2010) 扩展至高维数据，在短期时间序列和高维宏观经济预测方面表现优于经典 VAR 模型。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI: 10.1080/07350015.1986.10509491 ↗
- Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian Vector Auto Regressions. Journal of Applied Econometrics, 25(1), 71-92. DOI: 10.1002/jae.1137 ↗
如何引用本页
ScholarGate. (2026, June 1). Bayesian Vector Autoregression. ScholarGate. https://scholargate.app/zh/econometrics/bvar
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- 门限向量自回归（TVAR）和光滑转换向量自回归（STVAR）计量经济学↔ compare
- 向量自回归 (VAR) 模型计量经济学↔ compare