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.