The Impossibility of Testing for Dependence Using Kendall’s Ƭ Under Missing Data of Unknown Form

Oliver R. Cutbill and Rami V. Tabri

No 2022-03, Working Papers from University of Sydney, School of Economics

Abstract: This paper discusses the statistical inference problem associated with testing for dependence between two continuous random variables using Kendall’s Ƭ in the context of the missing data problem. We prove the worst-case identified set for this measure of association always includes zero. The consequence of this result is that robust inference for dependence using Kendall’s Ƭ, where robustness is with respect to the form of the missingness-generating process, is impossible.

Keywords: Impossible Inference; Statistical Dependence; Kendall’s Ƭ; Partial Identification; Missing Data (search for similar items in EconPapers)
Date: 2022-02
New Economics Papers: this item is included in nep-ecm
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