Abstract

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.

摘要

本文专门介绍了基于Bonferroni边界（对Bonferroni边界的细化）的临界值（CV）方法，该方法由McCloskey提出，用于在一般线性回归模型中选择一致的模型后得出的推理问题。选择后问题的制定是为了模仿常见的经验实践，并且适用于横截面和时间序列环境。我们提供了在这种情况下构建CV的算法，并为所得测试建立了统一的渐近大小结果。在对教室规模对考试成绩的影响的经验应用中说明了CV的实际实施。