| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w10975 |
| 来源ID | Working Paper 10975 |
| Semiparametric Causality Tests Using the Policy Propensity Score | |
| Joshua D. Angrist; Guido M. Kuersteiner | |
| 发表日期 | 2004-12-13 |
| 出版年 | 2004 |
| 语种 | 英语 |
| 摘要 | Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a semi-parametric test for causality in models linking a binary treatment or policy variable with unobserved potential outcomes. The procedure is semiparametric in the sense that we model the process determining treatment -- the policy propensity score -- but leave the model for outcomes unspecified. This general approach is motivated by the notion that we typically have better prior information about the policy determination process than about the macro-economy. A conceptual innovation is that we adapt the cross-sectional potential outcomes framework to a time series setting. This leads to a generalized definition of Sims (1980) causality. We also develop a test for full conditional independence, in contrast with the usual focus on mean independence. Our approach is illustrated using data from the Romer and Romer (1989) study of the relationship between the Federal reserve's monetary policy and output. |
| 主题 | Econometrics ; Estimation Methods ; Macroeconomics ; Monetary Policy |
| URL | https://www.nber.org/papers/w10975 |
| 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/568609 |
| 推荐引用方式 GB/T 7714 | Joshua D. Angrist,Guido M. Kuersteiner. Semiparametric Causality Tests Using the Policy Propensity Score. 2004. |
| 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 | ||
| w10975.pdf（620KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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