Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/189737
Authors: 
Kitagawa, Toru
Nybom, Martin
Stuhler, Jan
Year of Publication: 
2018
Series/Report no.: 
cemmap working paper No. CWP28/18
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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).
Subjects: 
Errors-in-variables
Spearman's rank correlation
Kendall's tau
Small variance approximation
Intergenerational mobility
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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