摘要翻译：
在线性面板数据模型中，考虑了一个具有加性、未观察到的个体效应和时间效应，以及大量附加的时变混杂变量的少数感兴趣变量的系数的推论。我们允许这些附加的混杂变量的个数大于样本量，并且假定这些混杂变量除了不受限制的时间和个别的特定效应外，还由少量的公因子和高维弱相关扰动产生。我们允许这些因素和干扰都与结果变量和其他感兴趣的变量有关。为了使信息推理变得可行，我们规定，没有被时间特定效应、个体特定效应或公共因素所捕获的那部分混杂变量的贡献可以被相对较少的恒等式未知的项所捕获。在此框架内，我们给出了一种基于因子提取和lasso回归的简便计算算法，并证明了该算法具有良好的渐近性。我们还提供了一个简单的k步引导过程，可以用来构造关于感兴趣参数的推理语句，并证明了它的渐近有效性。所建议的引导程序可能在当前上下文之外具有实质性的独立兴趣，因为所建议的引导程序可能容易地适用于涉及套索变量选择后的推理的其他上下文，并且其有效性的证明需要一些新的技术论证。我们还提供了关于我们的程序性能的模拟证据，并说明了它在两个经验应用中的使用。
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英文标题：
《The Factor-Lasso and K-Step Bootstrap Approach for Inference in
  High-Dimensional Economic Applications》
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作者：
Christian Hansen and Yuan Liao
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最新提交年份：
2016
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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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英文摘要：
  We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual and time specific effects and a large number of additional time-varying confounding variables. We allow the number of these additional confounding variables to be larger than the sample size, and suppose that, in addition to unrestricted time and individual specific effects, these confounding variables are generated by a small number of common factors and high-dimensional weakly-dependent disturbances. We allow that both the factors and the disturbances are related to the outcome variable and other variables of interest. To make informative inference feasible, we impose that the contribution of the part of the confounding variables not captured by time specific effects, individual specific effects, or the common factors can be captured by a relatively small number of terms whose identities are unknown. Within this framework, we provide a convenient computational algorithm based on factor extraction followed by lasso regression for inference about parameters of interest and show that the resulting procedure has good asymptotic properties. We also provide a simple k-step bootstrap procedure that may be used to construct inferential statements about parameters of interest and prove its asymptotic validity. The proposed bootstrap may be of substantive independent interest outside of the present context as the proposed bootstrap may readily be adapted to other contexts involving inference after lasso variable selection and the proof of its validity requires some new technical arguments. We also provide simulation evidence about performance of our procedure and illustrate its use in two empirical applications.
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PDF链接：
https://arxiv.org/pdf/1611.09420