Quantile Regression: Analyzing Changes in Distributions Instead of Means
Quantile Regression: Analyzing Changes in Distributions Instead of Means
复制标题
分位数回归：分析分布的变化而不是均值
DOI：
10.1007/978-3-319-12835-1_8
复制
发表时间：
2015
影响因子：
3.2
通讯作者：
Stephen R. Porter
中科院分区：
文献类型：
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作者：
Stephen R. Porter
虽然多元回归一直是高等教育研究人员流行的统计选择，但它只能告诉我们自变量对 y 均值的影响。然而，在许多应用中，我们希望了解 y 整个分布的影响，而不仅仅是 y 的平均值。分位数回归提供了一种告诉我们这种效应的方法，尽管解释可能会根据是否使用条件分位数回归或无条件分位数回归而有所不同。本章回顾了条件和无条件分位数回归，重点是后者通过最近影响函数估计，假设自变量的外生性，以及工具变量分位数治疗效果估计器，假设治疗的内生性。讨论了围绕解释、估计、敏感性分析和结果呈现的问题，使用 2004 年全国高等教育教师调查来估计男女工资差异以及教师工会对教师工资的影响。
While multiple regression has been a popular statistical choice for postsecondary researchers, it can only tell us the effect of an independent variable on the mean of y. In many applications, however, we would like to know the effect across the entire distribution of y, not just the mean of y. Quantile regression provides one way of telling us this effect, although the interpretation can vary depending upon whether conditional or unconditional quantile regression is used.This chapter reviews conditional and unconditional quantile regression, with an emphasis on the latter as estimated via the recentered influence function, assuming exogeneity of the independent variables, and the instrumental variables quantile treatment effect estimator, assuming endogeneity of treatment. Issues around interpretation, estimation, sensitivity analyses, and presentation of results are discussed, using the 2004 National Survey of Postsecondary Faculty to estimate male-female salary differentials and the effect of faculty unions on faculty salaries.