摘要翻译:
政策评估是经济数据分析的核心,但鉴于进行受控实验的机会有限,经济学家大多使用观察数据。在潜在结果框架中,面板数据方法(Hsiao,Ching and Wan,2012)通过利用面板数据中横截面单元之间的相关性来构建反事实。然而,在数据丰富的环境中,当许多可能的控制可以由研究人员支配时,横断面控制单元的选择是其实现的关键步骤,但仍未解决。我们提出了前向选择方法来选择控制单元,并建立了后向选择推理的有效性。我们的渐近框架允许可能控制的数量比时间维增长得快得多。易于实现的算法及其理论保证将面板数据方法扩展到大数据设置。
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英文标题:
《Forward-Selected Panel Data Approach for Program Evaluation》
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作者:
Zhentao Shi, Jingyi Huang
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最新提交年份:
2021
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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英文摘要:
Policy evaluation is central to economic data analysis, but economists mostly work with observational data in view of limited opportunities to carry out controlled experiments. In the potential outcome framework, the panel data approach (Hsiao, Ching and Wan, 2012) constructs the counterfactual by exploiting the correlation between cross-sectional units in panel data. The choice of cross-sectional control units, a key step in its implementation, is nevertheless unresolved in data-rich environment when many possible controls are at the researcher's disposal. We propose the forward selection method to choose control units, and establish validity of the post-selection inference. Our asymptotic framework allows the number of possible controls to grow much faster than the time dimension. The easy-to-implement algorithms and their theoretical guarantee extend the panel data approach to big data settings.
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PDF链接:
https://arxiv.org/pdf/1908.05894