Gaussian Rank Correlation and Regression

Dante Amengual, Enrique Sentana, Zhanyuan Tian
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引用次数: 2

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.
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高斯秩相关与回归
我们研究了高斯秩的Pearson相关系数的统计性质,以及应用于这些秩的高斯秩回归—OLS。我们表明,当真copula为高斯且边缘是非参数估计时,这些过程是完全有效的,并且对于它们的总体类似物保持一致。我们将它们与Spearman和Pearson相关性及其在理论上和广泛的蒙特卡罗模拟中的回归对应物进行比较。对美国各州的移民和增长、增强型索洛增长模型以及个股收益的动量和逆转效应的实证应用证实,高斯秩过程对异常值不敏感。
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