Gaussian Rank Correlation and Regression
ISBN: 978-1-80262-066-5, eISBN: 978-1-80262-065-8
Publication date: 18 January 2022
Abstract
We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models 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 analogs 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
Acknowledgements
Acknowledgments
Some of the material in this chapter originally appeared in Sections 2.3, 2.5 and 2.7 of Amengual and Sentana (2015). We are grateful to Xinyue Bei and participants at Encuentro de la SEU (Montevideo, 2018), 8th ICEEE Conference (Lecce, 2019), AMES (Xiamen, 2019), CMES (Guangzhou, 2019), IAAE (Nicosia, 2019), ESEM (Manchester, 2019) and SAEe (Alicante, 2019) for their helpful comments, discussions and suggestions. An anonymous referee has also helped us greatly improve the chapter. Of course, the usual caveat applies. Financial support from the Spanish Ministry of Economy, Industry and Competitiveness through grant ECO 2017-89689 and the Santander CEMFI Research Chair in Banking and Finance is gratefully acknowledged.
Citation
Amengual, D., Sentana, E. and Tian, Z. (2022), "Gaussian Rank Correlation and Regression", Chudik, A., Hsiao, C. and Timmermann, A. (Ed.) Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology (Advances in Econometrics, Vol. 43B), Emerald Publishing Limited, Leeds, pp. 269-306. https://doi.org/10.1108/S0731-90532021000043B012
Publisher
:Emerald Publishing Limited
Copyright © 2022 Dante Amengual, Enrique Sentana and Zhanyuan Tian