Unconditional quantile regression for panel data with fixed effects can be tricky, but a new method has been developed to tackle this challenge. This method allows for estimating quantile treatment effects in panel data while considering fixed effects, without actually estimating them. The key idea is to use differences in covariates or instruments for identification, making the estimator consistent for small T. This means that researchers can now interpret the parameters of interest in the same way as they would in cross-sectional studies, even with panel data.