| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26250 |
| 来源ID | Working Paper 26250 |
| Panel Data and Experimental Design | |
| Fiona Burlig; Louis Preonas; Matt Woerman | |
| 发表日期 | 2019-09-09 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | How should researchers design panel data experiments? We analytically derive the variance of panel estimators, informing power calculations in panel data settings. We generalize Frison and Pocock (1992) to fully arbitrary error structures, thereby extending McKenzie (2012) to allow for non-constant serial correlation. Using Monte Carlo simulations and real world panel data, we demonstrate that failing to account for arbitrary serial correlation ex ante yields experiments that are incorrectly powered under proper inference. By contrast, our “serial-correlation-robust” power calculations achieve correctly powered experiments in both simulated and real data. We discuss the implications of these results, and introduce a new software package to facilitate proper power calculations in practice. |
| 主题 | Other ; History of Economic Thought ; Econometrics ; Estimation Methods ; Experimental Design ; Development and Growth ; Development ; Environmental and Resource Economics ; Energy |
| URL | https://www.nber.org/papers/w26250 |
| 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/583922 |
| 推荐引用方式 GB/T 7714 | Fiona Burlig,Louis Preonas,Matt Woerman. Panel Data and Experimental Design. 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 | ||
| w26250.pdf（833KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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