Chudik, Kapetanios, & Pesaran (Econometrica 2018, 86, 1479-1512) propose a one covariate at a time, multiple testing (OCMT) approach to variable selection in high-dimensional linear regression models as an alternative approach to penalised regression. We offer a narrow replication of their key OCMT results based on the Stata software instead of the original MATLAB routines. Using the new user-written Stata commands baing and ocmt, we find results that match closely those reported by these authors in their Monte Carlo simulations. In addition, we replicate exactly their findings in the empirical illustration, which relate to top five variables with highest inclusion frequencies based on the OCMT selection method.

中文翻译：

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一次一个协变量，高维线性回归模型中变量选择的多重测试方法：狭义上的复制

Chudik、Kapetanios 和 Pesaran (Econometrica 2018, 86, 1479-1512) 提出了一次一个协变量、多重检验 (OCMT) 方法来在高维线性回归模型中进行变量选择，作为惩罚回归的替代方法。我们提供基于 Stata 软件而不是原始 MATLAB 例程的关键 OCMT 结果的狭窄复制。使用新的用户编写的 Stata 命令baing和ocmt，我们发现结果与这些作者在蒙特卡罗模拟中报告的结果非常匹配。此外，我们在实证插图中准确复制了他们的发现，这些发现与基于 OCMT 选择方法的包含频率最高的前五个变量有关。