| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP14914 |
| DP14914 Gaussian rank correlation and regression | |
| Dante Amengual; ENRIQUE SENTANA; Zhanyuan Tian | |
| 发表日期 | 2020-06-21 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Financial Economics |
| 关键词 | Copula Growth regressions Migration Misspecification Momentum Robustness Short-term reversals |
| URL | https://cepr.org/publications/dp14914 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/543855 |
| 推荐引用方式 GB/T 7714 | Dante Amengual,ENRIQUE SENTANA,Zhanyuan Tian. DP14914 Gaussian rank correlation and regression. 2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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