Measurement error and rank correlations [report]

Martin Nybom, Toru Kitagawa, Jan Stuhler
2018 unpublished
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
more » ... mpirical 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).
doi:10.1920/wp.cem.2081.2818 fatcat:miq26ixl2vb4fpemvxbrgw4mle