 Inference on Consensus Ranking of DistributionsJournal of Business & Economic Statistics, 2024, vol. 42, issue 3, 839-850 Abstract: Instead of testing for unanimous agreement, I propose learning how broad of a consensus favors one distribution over another (of earnings, productivity, asset returns, test scores, etc.). Specifically, given a sample from each of two distributions, I propose statistical inference methods to learn about the set of utility functions for which the first distribution has higher expected utility than the second distribution. With high probability, an “inner” confidence set is contained within this true set, while an “outer” confidence set contains the true set. Such confidence sets can be formed by inverting a proposed multiple testing procedure that controls the familywise error rate. Theoretical justification comes from empirical process results, given that very large classes of utility functions are generally Donsker (subject to finite moments). The theory additionally justifies a uniform (over utility functions) confidence band of expected utility differences, as well as tests with a utility-based “restricted stochastic dominance” as either the null or alternative hypothesis. Simulated and empirical examples illustrate the methodology. Date: 2024 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:42:y:2024:i:3:p:839-850 Ordering information: This journal article can be ordered from DOI: 10.1080/07350015.2023.2252040 Access Statistics for this article Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals |
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