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摘要翻译：
三维面板模型在实证分析中得到了广泛的应用。研究人员为三维面板使用固定效果的各种组合。当一个人强加了一个吝啬的模型，而真正的模型是丰富的，那么它就会引起错误规范的偏差。当一个人使用一个丰富的模型，而真正的模型是吝啬的，那么它会产生比必要的更大的标准误差。因此，研究人员了解正确的模型是有用的。在这一点上，Lu，Mia，Su(2018)提出了模型选择的方法。我们通过提出回归参数的后选择推断方法来推进这一文献。尽管我们使用套索技术作为模型选择的手段，但我们的假设允许许多甚至所有的固定效应都是非零的。仿真研究表明，该方法比欠拟合固定效应估计器精度更高，比过拟合固定效应估计器效率更高，并允许与oracle估计器一样精确的推断。
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英文标题：
《Post-Selection Inference in Three-Dimensional Panel Data》
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作者：
Harold D. Chiang and Joel Rodrigue and Yuya Sasaki
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最新提交年份：
2019
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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：
Three-dimensional panel models are widely used in empirical analysis. Researchers use various combinations of fixed effects for three-dimensional panels. When one imposes a parsimonious model and the true model is rich, then it incurs mis-specification biases. When one employs a rich model and the true model is parsimonious, then it incurs larger standard errors than necessary. It is therefore useful for researchers to know correct models. In this light, Lu, Miao, and Su (2018) propose methods of model selection. We advance this literature by proposing a method of post-selection inference for regression parameters. Despite our use of the lasso technique as means of model selection, our assumptions allow for many and even all fixed effects to be nonzero. Simulation studies demonstrate that the proposed method is more precise than under-fitting fixed effect estimators, is more efficient than over-fitting fixed effect estimators, and allows for as accurate inference as the oracle estimator.
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PDF链接：
https://arxiv.org/pdf/1904.00211
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