Estimation and inference of treatment effects with L2-boosting in high-dimensional settings
Estimation and inference of treatment effects with L2-boosting in high-dimensional settings
复制标题
高维环境中 L2 增强治疗效果的估计和推断
DOI：
10.1016/j.jeconom.2022.02.005
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发表时间：
2017
影响因子：
6.3
通讯作者：
M. Spindler
中科院分区：
文献类型：
--
作者：
Ye Luo;M. Spindler
实证研究人员越来越多地面对包含许多控制或工具变量的丰富数据集，这使得选择合适的方法来选择变量变得至关重要。在本文中，我们提供了用后置或正交L 2-Booing进行变量选择后的有效推断的结果。我们在许多控制变量和工具变量模型中进行选择后，考虑处理效果。为了实现这一点，我们在类似于Lasso的高维环境下，即在不假定β-min条件的近似稀疏性下，建立了迭代后L 2-Booing和正交L 2-Booing的收敛速度的新结果。这些结果被推广到2SLS框架，为治疗效果分析提供了有效的推断。我们给出了大量的仿真结果，并与Lasso方法进行了比较。在一个实证应用中，我们使用我们提出的方法构建了有效的内部投资工具，以估计并购前美国银行分支机构网络重叠对收购方银行并购后股票回报的影响。
Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables, making it essential to choose an appropriate approach to variable selection. In this paper, we provide results for valid inference after post-or orthogonal L 2-boosting is used for variable selection. We consider treatment effects after selecting among many control variables and instrumental variable models with potentially many instruments. To achieve this, we establish new results for the rate of convergence of iterated post-L 2-boosting and orthogonal L 2-boosting in a high-dimensional setting similar to Lasso, ie, under approximate sparsity without assuming the beta-min condition. These results are extended to the 2SLS framework and valid inference is provided for treatment effect analysis. We give extensive simulation results for the proposed methods and compare them with Lasso. In an empirical application, we construct efficient IVs with our proposed methods to estimate the effect of pre-merger overlap of bank branch networks in the US on the post-merger stock returns of the acquirer bank.
影响因子：
1.7
作者：
Hepp, Tobias;Schmid, Matthias;Mayr, Andreas
通讯作者：
Mayr, Andreas