方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 贝叶斯向量自回归 (BVAR)× | 因子增广向量自回归模型 (FAVAR)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1986 | 2005 |
| 提出者≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes |
| 类型≠ | Bayesian multivariate time-series model | Multivariate time-series model |
| 开创性文献≠ | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ | Bernanke, B. S., Boivin, J. & Eliasz, P. (2005). Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. The Quarterly Journal of Economics, 120(1), 387-422. DOI ↗ |
| 别名≠ | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR) |
| 相关≠ | 5 | 4 |
| 摘要≠ | Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts. | FAVAR is a multivariate time-series model that first compresses information from a very large set of variables into a few common factors, then includes those factors alongside the observed variables in a vector autoregression. It was introduced by Bernanke, Boivin and Eliasz in 2005 to study monetary policy using hundreds of macroeconomic indicators at once. |
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