Measurement error and rank correlations
Toru Kitagawa (),
Martin Nybom () and
Jan Stuhler ()
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Toru Kitagawa: Institute for Fiscal Studies and cemmap and University College London
No CWP28/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
This paper characterizes and proposes a method to correct for errors-in-variables biases in the estimation of rank correlation coeffcients (Spearman's ? and Kendall's t). 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).
Keywords: Errors-in-variables; Spearman's rank correlation; Kendall's tau; Small variance approximation; Intergenerational mobility. (search for similar items in EconPapers)
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