摘要翻译：
本文讨论了计量经济学中高维稀疏(HDS)回归模型的估计和推断方法。高维稀疏模型出现在许多回归子（或级数项）可用的情况下，回归函数由一组简洁但未知的回归子很好地逼近。后一个条件使得通过搜索近似正确的回归子集来有效地估计整个回归函数成为可能。我们讨论了基于$\ell_1$-惩罚的识别这组回归子和估计它们的系数的方法，并描述了关键的理论结果。为了捕捉现实的实际情况，我们明确允许回归子的不完全选择，并研究这种不完全选择对估计和推断结果的影响。本文的主要部分集中在HDS模型和方法在工具变量模型和部分线性模型中的应用。我们给出了这些模型的一组新的推论结果，并举例说明了它们在学校教育回报和成长回归中的应用。
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英文标题：
《Inference for High-Dimensional Sparse Econometric Models》
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作者：
Alexandre Belloni and Victor Chernozhukov and Christian Hansen
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最新提交年份：
2011
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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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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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is well-approximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regression function effectively by searching for approximately the right set of regressors. We discuss methods for identifying this set of regressors and estimating their coefficients based on $\ell_1$-penalization and describe key theoretical results. In order to capture realistic practical situations, we expressly allow for imperfect selection of regressors and study the impact of this imperfect selection on estimation and inference results. We focus the main part of the article on the use of HDS models and methods in the instrumental variables model and the partially linear model. We present a set of novel inference results for these models and illustrate their use with applications to returns to schooling and growth regression.
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PDF链接：
https://arxiv.org/pdf/1201.0220