A Simple Quantile Regression Model Linking Micro Outcomes to Macro Covariates
提出一种新的位置-尺度分位数回归模型，用重复截面数据研究宏观变量对微观结果分布的影响，通过转化为均值回归用最小二乘法估计，提升计算效率并保持稳健性。
ABSTRACT 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.