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This paper characterizes and proposes a method to correct for errors-in-variables biases in the estimation of rank correlation coeffcients (Spearman's ρ and Kendall's τ). We first investigate a set of suffcient 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).
Authors
Research Associate University College London and Brown University
Toru is a Research Associate of the IFS, a Professor of Economics at UCL and an Associate Professor in the Department of Economics at Brown University
Martin Nybom
Jan Stuhler
Working Paper details
- DOI
- 10.1920/wp.cem.2081.2818
- Publisher
- The IFS
Suggested citation
T, Kitagawa and M, Nybom and J, Stuhler. (2018). Measurement error and rank correlations. London: The IFS. Available at: https://ifs.org.uk/publications/measurement-error-and-rank-correlations (accessed: 8 May 2024).
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