A Unifying Framework for Testing Shape Restrictions

TL;DR

This paper introduces a comprehensive framework for testing shape restrictions using the Wald principle, ensuring asymptotic size control and consistency, with practical applications demonstrated through simulations and labor market data analysis.

Contribution

It develops a unifying testing framework based on the Wald principle, analyzes shape enforcing operators, and demonstrates applicability in complex parameter spaces.

Findings

The proposed test maintains asymptotic size control.

Rearrangement operator is inapplicable due to lack of convexity.

Monte Carlo simulations show the test performs well.

Abstract

This paper makes the following original contributions. First, we develop a unifying framework for testing shape restrictions based on the Wald principle. The test has asymptotic uniform size control and is uniformly consistent. Second, we examine the applicability and usefulness of some prominent shape enforcing operators in implementing our framework. In particular, in stark contrast to its use in point and interval estimation, the rearrangement operator is inapplicable due to a lack of convexity. The greatest convex minorization and the least concave majorization are shown to enjoy the analytic properties required to employ our framework. Third, we show that, despite that the projection operator may not be well-defined/behaved in general parameter spaces such as those defined by uniform norms, one may nonetheless employ a powerful distance-based test by applying our framework. Monte Carlo simulations confirm that our test works well. We further showcase the empirical relevance by investigating the relationship between weekly working hours and the annual wage growth in the high-end labor market.