Conditional Rank-Rank Regression
条件秩对秩回归AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要以条件分布和条件秩刻画收入关联，并采用分布回归估计代际收入持续性。
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条件秩对秩回归
- 研究问题
- 秩对秩回归加入协变量或先残差化后，系数可能难以解释；如何构造可解释为协变量所定义群体内部平均关联的指标？
- 方法与思路
- 作者以条件秩替代无条件秩，使回归系数对应条件 Spearman 相关系数的平均值，并用分布回归估计及建立大样本推断框架。
- 材料陈述的贡献
- 摘要称新系数刻画平均组内持续性；瑞士收入流动应用显示父子持续性高于父女，组内持续性分别为总体持续性的 62% 与 52%。
- 阅读关注点
- 应核对条件秩在离散收入或并列值下的定义、条件相关的平均方式，以及分布回归估计误差如何进入最终系数的推断。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明完整估计条件与实证控制设定，条件关联不能直接解释为因果效应。
原文摘要
Rank-rank regression is commonly employed in economic research as a way of capturing the relationship between two economic variables. The slope of this regression is the Spearman rank correlation, a classical measure of association. However, in many applications it is common practice to include covariates to account for differences in association levels between groups as defined by the values of these covariates. This is either done by including the covariates or by modeling the residuals obtained after partialing out the impact of the covariates. In each of these instances the resulting rank-rank regression coefficients can be difficult to interpret. We propose the conditional rank-rank regression, which uses conditional ranks instead of unconditional ranks, to measure average within-group persistence. The coefficient of this new regression corresponds to the average Spearman rank correlation conditional on the covariates, a natural summary measure of within-group association. We develop a flexible estimation approach using distribution regression and establish a theoretical framework for large sample inference. An empirical study on intergenerational income mobility in Switzerland demonstrates the advantages of this approach. The study reveals stronger intergenerational persistence between fathers and sons compared to fathers and daughters, with the within-group persistence being 62% of the overall income persistence for sons and 52% for daughters. Smaller families and those with highly educated fathers exhibit greater persistence in economic status.