| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w1202 |
| 来源ID | Working Paper 1202 |
| Forecasting and Conditional Projection Using Realistic Prior Distributions | |
| Thomas Doan; Robert B. Litterman; Christopher A. Sims | |
| 发表日期 | 1983-09-01 |
| 出版年 | 1983 |
| 语种 | 英语 |
| 摘要 | This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates.We provide unconditional forecasts as of 1982:12 and 1983:3.We also describe how a model such as this can be used to make conditional projections and to analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12.While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help inevaluating causal hypotheses, without containing any such hypotheses themselves. |
| URL | https://www.nber.org/papers/w1202 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/558426 |
| 推荐引用方式 GB/T 7714 | Thomas Doan,Robert B. Litterman,Christopher A. Sims. Forecasting and Conditional Projection Using Realistic Prior Distributions. 1983. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w1202.pdf（833KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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