| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w25132 |
| 来源ID | Working Paper 25132 |
| Matrix Completion Methods for Causal Panel Data Models | |
| Susan Athey; Mohsen Bayati; Nikolay Doudchenko; Guido Imbens; Khashayar Khosravi | |
| 发表日期 | 2018-10-08 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | In this paper we study methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during a subset of periods, and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations. We develop a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to predict the “missing” elements of the matrix, corresponding to treated units/periods. The approach estimates a matrix that well-approximates the original (incomplete) matrix, but has lower complexity according to the nuclear norm for matrices. From a technical perspective, we generalize results from the matrix completion literature by allowing the patterns of missing data to have a time series dependency structure. We also present novel insights concerning the connections between the matrix completion literature, the literature on interactive fixed effects models and the literatures on program evaluation under unconfoundedness and synthetic control methods. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w25132 |
| 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/582806 |
| 推荐引用方式 GB/T 7714 | Susan Athey,Mohsen Bayati,Nikolay Doudchenko,et al. Matrix Completion Methods for Causal Panel Data Models. 2018. |
| 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 | ||
| w25132.pdf（1144KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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