New Publication: Using parametric quantile regression to investigate determinants of unemployment duration

Read about Using parametric quantile regression to investigate determinants of unemployment duration, published in Empirical Economics by Lorenzo Corsini and Paolo Dyno Frumento.

Abstract:

We estimate conditional quantiles of unemployment duration, using a method for interval-censored quantile regression. We apply a modeling approach in which the regression coefficients are described by parametric functions. Compared with standard quantile regression, in which quantiles are calculated one at a time, the proposed method drastically simplifies estimation and inference and makes it simpler to report and interpret the results. We discuss goodness-of-fit measures, present a simulation study, and describe the R package qrcm that provides the necessary functions for estimation, inference, and prediction. Our results show that age, education, and other individual and household-level covariates significantly affect unemployment duration. While the estimated effects generally align with the existing literature, most predictors exhibit heterogeneous effects across quantiles, suggesting a complexity that standard location-scale or proportional hazards models may fail to capture.