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Measurement error and rank correlations

Kitagawa, T; Nybom, M; Stuhler, J; (2018) Measurement error and rank correlations. (cemmap working paper 28/18). Institute for Fiscal Studies (IFS): London, UK. Green open access

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Abstract

This paper characterizes and proposes a method to correct for errors-in-variables biases in the estimation of rank correlation coefficients (Spearman’s ρ and Kendall’s τ ). We first investigate a set of sufficient conditions under which measurement errors bias the sample rank correlations toward zero. We then provide a feasible nonparametric bias-corrected estimator based on the technique of small error variance approximation. We assess its performance in simulations and an empirical application, using rich Swedish data to estimate intergenerational rank correlations in income. The method performs well in both cases, lowering the mean squared error by 50-85 percent already in moderately sized samples (n = 1, 000).

Type: Working / discussion paper
Title: Measurement error and rank correlations
Open access status: An open access version is available from UCL Discovery
DOI: 10.1920/wp.cem.2081.2818
Publisher version: https://doi.org/10.1920/wp.cem.2081.2818
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Errors-in-variables, Spearman’s rank correlation, Kendall’s tau, Small variance approximation, Intergenerational mobility
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10087171
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