分位数回归模型解读：各分位数是否对应不同收入层级的技术问询

Great question—this is a common and important point to clarify when you're first working with quantile regression, especially for income data!

To cut straight to the chase: Yes, the 0.10, 0.50, and 0.90 quantile models you’ve estimated do directly correspond to distinct income level groups—here’s how to map them:

• 0.10 Quantile (10th Percentile): This model represents the low-income worker group. It estimates how your explanatory variables (education years, work experience, education type, etc.) impact the income of workers who fall in the bottom 10% of the conditional income distribution (i.e., workers with similar observed characteristics but lower relative earnings).
• 0.50 Quantile (Median): This is your middle-income worker model. Unlike OLS regression (which focuses on the conditional mean), the median quantile zeroes in on the worker at the exact midpoint of the conditional income distribution—this is often a better measure of "typical" worker earnings when income data is skewed (which it almost always is!).
• 0.90 Quantile (90th Percentile): This corresponds to the high-income worker group. It captures how your predictors affect earnings for workers in the top 10% of the conditional income distribution—this is where you might find interesting heterogeneities, like whether advanced education has a larger marginal return for high-earning workers versus low-earning ones.

A Key Nuance to Keep in Mind #

It’s important to note that these are conditional income levels, not absolute income thresholds. That means the 0.10 quantile isn’t just "all workers in the bottom 10% of overall income"—it’s workers who, given their specific education, experience, and other traits, earn less than 90% of their peers with identical characteristics. This is what makes quantile regression so powerful for addressing heteroscedasticity: it accounts for how the effect of predictors can vary across different segments of the income distribution, rather than forcing a one-size-fits-all average effect (like OLS does).

内容的提问来源于stack exchange，提问作者Fajar Wisga Permana