Abstract: For nearly 25 years, advances in longitudinal data and quantile regression were developed almost completely in parallel, with no intersection until the work by Koenker (2004). The early theoretical work in statistics and economics raised more questions than answers, but it encouraged the development of several promising new approaches and research that offered a better understanding of the challenges and possibilities in the intersection of the literatures. Panel data quantile regression allows the estimation of effects that are heterogeneous throughout the conditional distribution of the response variable, while controlling for individual and time specific confounders. This type of heterogeneous effect is not be well summarized by the average effect. For instance, the relationship between number of students in a class and average educational achievement has been extensively investigated, but research also shows that class size affects differently low-achieving and high-achieving students. The recent advances in panel data include several methods and algorithms that created opportunities for more informative and robust empirical analysis in models with subject heterogeneity and factor structure.