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贝叶斯向量自回归模型 (BVAR)

贝叶斯向量自回归 (BVAR) 模型通过纳入关于模型系数的先验信念,扩展了经典的向量自回归框架。先验——最常见的是明尼苏达先验——将向量自回归系数向经济上合理的数值收缩,从而显著减少过拟合,并提高样本外预测精度,即使在变量数量很多的情况下也是如此。

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来源

  1. Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI: 10.1080/07474938408800053
  2. Koop, G., & Korobilis, D. (2010). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends in Econometrics, 3(4), 267–358. DOI: 10.1561/0800000013

如何引用本页

ScholarGate. (2026, June 3). Bayesian Vector Autoregression Model. ScholarGate. https://scholargate.app/zh/econometrics/bayesian-var-model

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被引用于

ScholarGateBayesian VAR model (Bayesian Vector Autoregression Model). 于 2026-06-15 检索自 https://scholargate.app/zh/econometrics/bayesian-var-model · 数据集: https://doi.org/10.5281/zenodo.20539026