Measurement error and rank correlations

Arbeitspapier

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).

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: cemmap working paper ; No. CWP28/18

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: Errors-in-variables ; Spearman's rank correlation ; Kendall's tau ; Small variance approximation ; Intergenerational mobility

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Kitagawa, Toru ; Nybom, Martin ; Stuhler, Jan
• Ereignis: Veröffentlichung
• (wer): Centre for Microdata Methods and Practice (cemmap)
• (wo): London
• (wann): 2018

Weitere Informationen

• DOI: doi:10.1920/wp.cem.2081.2818
• Handle: http://hdl.handle.net/10419/189737
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ