Asymptotically Uniform Tests After Consistent Model Selection in the Linear Regression Model
将基于Bonferroni边界的临界值方法专门化到一般线性回归模型的一致模型选择后推断问题，提供构造临界值的算法并证明检验的渐近一致大小性质，适用于横截面和时间序列数据，并以班级规模对考试成绩影响的实证应用说明实施。
This article specializes the critical value (CV) methods that are based upon (refinements of) Bonferroni bounds, introduced by McCloskey to a problem of inference after consistent model selection in a general linear regression model. The post-selection problem is formulated to mimic common empirical practice and is applicable to both cross-sectional and time series contexts. We provide algorithms for constructing the CVs in this setting and establish uniform asymptotic size results for the resulting tests. The practical implementation of the CVs is illustrated in an empirical application to the effect of classroom size on test scores.