| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w22423 |
| 来源ID | Working Paper 22423 |
| Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech | |
| Matthew Gentzkow; Jesse M. Shapiro; Matt Taddy | |
| 发表日期 | 2016-07-18 |
| 出版年 | 2016 |
| 语种 | 英语 |
| 摘要 | We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We show that standard approaches suffer from a severe finite-sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson’s party from a single utterance. Our estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s after remaining low and relatively constant over the preceding century. |
| 主题 | Microeconomics ; Welfare and Collective Choice |
| URL | https://www.nber.org/papers/w22423 |
| 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/580097 |
| 推荐引用方式 GB/T 7714 | Matthew Gentzkow,Jesse M. Shapiro,Matt Taddy. Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech. 2016. |
| 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 | ||
| w22423.pdf（686KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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