Working Paper: CEPR ID: DP14914
Authors: Dante Amengual; Enrique Sentana; Zhanyuan Tian
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
JEL Codes: C13; C46; G14; O47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Migration rates (F22) | Growth rates (O49) |
| Estimation method (C51) | Efficiency (D61) |
| Gaussian rank procedures (C29) | Sensitivity to outliers (C20) |
| Choice of method (C52) | Reliability of empirical results (C59) |