Analyzing the Application of Quantile Regression in Modeling Conditional Distributions and Income Inequality Data

Posted: Apr 30, 2020

• Mason Allen
• Grace Nelson
• Jack Williams

Abstract

The analysis of income inequality represents one of the most pressing challenges in contemporary economic and social research. Traditional econometric approaches to modeling income distributions have predominantly relied on mean regression techniques, which provide valuable insights into central tendencies but offer limited understanding of distributional heterogeneity. Quantile regression, introduced by Koenker and Bassett (1978), represents a paradigm shift in regression analysis by enabling the estimation of conditional quantile functions, thereby providing a more comprehensive characterization of the relationship between covariates and the response variable across the entire distribution. This research addresses the critical gap in current methodological approaches to income inequality analysis by developing and applying an innovative quantile regression framework that captures the nuanced ways in which socioeconomic factors influence different segments of the income distribution.