  Unconditional quantile regression (UQR) attracts attention in various fields
to investigate the impacts of explanatory variables on quantiles of the
marginal distribution of an explained variable. This study attempts to
introduce spatial dependence into the UQR within the framework of random
effects eigenvector spatial filtering, resulting in the model that we term the
spatially filtered UQR (SF-UQR). We then develop a computationally efficient
approach for SF-UQR estimation. Finally, the performance of the SF-UQR is
tested with a hedonic land price model for the Tokyo metropolitan area. SF-UQR
is implemented in an R package, "spmoran."
