This paper makes the following econometric contributions. First, we develop a unifying framework for testing shape restrictions based on the Wald principle. Second, we examine the applicability and usefulness of some prominent shape enforcing operators in implementing our test, including rearrangement and the greatest convex minorization (or the least concave majorization). In particular, the influential rearrangement operator is inapplicable due to a lack of convexity, while the greatest convex minorization is shown to enjoy the analytic properties required to employ our framework. The importance of convexity in establishing size control has been noted elsewhere in the literature. Third, we show that, despite that the projection operator may not be well-defined/behaved in general non-Hilbert parameter spaces (e.g., ones defined by uniform norms), one may nonetheless devise a powerful distance-based test by applying our framework. The finite sample performance of our test is evaluated through Monte Carlo simulations, and its empirical relevance is showcased by investigating the relationship between weekly working hours and the annual wage growth in the high-end labor market.
翻译:本文提出了以下计量经济学贡献。 首先,我们根据Wald 原则为测试形状限制制定统一框架。 其次,我们审视某些著名执行操作者实施测试的适用性和有用性,包括重新安排和最大的细化(或最小的细化)等。 特别是,有影响力的重新安排操作者由于缺乏共性而无法适用,而最大的细化显示其享有使用我们框架所需的分析性能。 文献的其他地方已经指出了在建立规模控制方面凝聚的重要性。 第三,我们表明,尽管预测操作者在一般非Hilbert参数空间(如统一规范界定的参数空间)可能没有很好地界定/进行,但人们仍可以通过应用我们的框架来设计一个强大的远程测试。 通过蒙特卡洛模拟来评估我们测试的有限样本性表现,并通过调查高端劳动力市场每周工作时间与年工资增长之间的关系来展示其经验相关性。