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