Gaussian Rank Correlation and Regression

Dante Amengual, Enrique Sentana and Zhanyuan Tian ()
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Zhanyuan Tian: Boston University, https://www.bu.edu/

Working Papers from CEMFI

Abstract: We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions - OLS applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogues otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model, and momentum and reversal effects in individual stock returns confi?rm that Gaussian rank procedures are insensitive to outliers.

Keywords: Copula; growth regressions; migration; misspecification; momentum; robustness; short-term reversals. (search for similar items in EconPapers)
JEL-codes: C13 C46 G14 O47 (search for similar items in EconPapers)
Date: 2020-06
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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https://www.cemfi.es/ftp/wp/2004.pdf (application/pdf)

Related works:
Chapter: Gaussian Rank Correlation and Regression (2022)
Working Paper: Gaussian rank correlation and regression (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:cmf:wpaper:wp2020_2004

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