| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26584 |
| 来源ID | Working Paper 26584 |
| Machine Labor | |
| Joshua Angrist; Brigham Frandsen | |
| 发表日期 | 2019-12-30 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | Machine learning (ML) is mostly a predictive enterprise, while the questions of interest to labor economists are mostly causal. In pursuit of causal effects, however, ML may be useful for automated selection of ordinary least squares (OLS) control variables. We illustrate the utility of ML for regression-based causal inference by using lasso to select control variables for estimates of effects of college characteristics on wages. ML also seems relevant for an instrumental variables (IV) first stage, since the bias of two-stage least squares can be said to be due to over-fitting. Our investigation shows, however, that while ML-based instrument selection can improve on conventional 2SLS estimates, split-sample IV, jackknife IV, and LIML estimators do better. In some scenarios, the performance of ML-augmented IV estimators is degraded by pretest bias. In others, nonlinear ML for covariate control creates artificial exclusion restrictions that generate spurious findings. ML does better at choosing control variables for models identified by conditional independence assumptions than at choosing instrumental variables for models identified by exclusion restrictions. |
| 主题 | Econometrics ; Estimation Methods ; Labor Economics |
| URL | https://www.nber.org/papers/w26584 |
| 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/584258 |
| 推荐引用方式 GB/T 7714 | Joshua Angrist,Brigham Frandsen. Machine Labor. 2019. |
| 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 | ||
| w26584.pdf（822KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
