Gaussian rank correlation and regression

Enrique Sentana, Dante Amengual and Zhanyuan Tian

No 14914, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 confirm 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-ore
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Citations: View citations in EconPapers (2)

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Related works:
Chapter: Gaussian Rank Correlation and Regression (2022)
Working Paper: Gaussian Rank Correlation and Regression (2020)
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