摘要翻译：
我们研究了lasso Logit的估计、逐点推理和同时推理，以及许多平均部分效应的置信区间。针对高维聚类抽样环境，我们提出了一种新的平均部分效应估计，并研究了它的渐近性质。文中还提供了与我们的渐近理论相兼容的实用惩罚选择。建议的估计器允许有效的推断，而不需要oracle属性。我们提供了易于实现的算法，用于聚类鲁棒的高维假设检验和使用乘数聚类引导构造同时有效的置信区间。我们将所提出的算法应用于Wu(2018)的文本回归模型，以检验互联网上性别语言的存在。
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英文标题：
《Many Average Partial Effects: with An Application to Text Regression》
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作者：
Harold D. Chiang
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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：
  We study estimation, pointwise and simultaneous inference, and confidence intervals for many average partial effects of lasso Logit. Focusing on high-dimensional, cluster-sampling environments, we propose a new average partial effect estimator and explore its asymptotic properties. Practical penalty choices compatible with our asymptotic theory are also provided. The proposed estimator allow for valid inference without requiring oracle property. We provide easy-to-implement algorithms for cluster-robust high-dimensional hypothesis testing and construction of simultaneously valid confidence intervals using a multiplier cluster bootstrap. We apply the proposed algorithms to the text regression model of Wu (2018) to examine the presence of gendered language on the internet.
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PDF链接：
https://arxiv.org/pdf/1812.09397