A Simple Quantile Regression Model Linking Micro Outcomes to Macro Covariates

• 成果类型: Article
• 署名作者: Chen, Xiaohong; Ju, Gaosheng; Li, Qi
• 署名单位: Yale University; Fudan University; Shanghai Institute of International Finance & Economics; Texas A&M University System; Texas A&M University College Station
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12765
• 发表日期: 2025
• 页码: 1341-1362
• 关键词: monetary-policy; inference; returns; INEQUALITY; MARKET; RISK; US; DECOMPOSITION; transmission; earnings

摘要

This paper introduces a new location-scale quantile regression model aimed at examining the effects of macroeconomic variables on the distribution of microeconomic outcomes using repeated cross-sectional data. The model can be converted into an equivalent mean regression, enabling quantile coefficient estimation through least squares. This transformation improves computational efficiency, simplifies statistical inference for large data sets, and maintains robustness against model misspecification. We establish the asymptotic properties of the estimator and investigate several extensions. Our applications demonstrate that stock returns and household large-scale expenditure growth rates respond differently across quantiles to expansionary monetary shocks and macroeconomic conditions, respectively.