Spatially filtered unconditional quantile regression: Application to a hedonic analysis

TL;DR

This paper introduces a novel spatially filtered unconditional quantile regression model that incorporates spatial dependence, providing an efficient estimation approach and demonstrating its application in land price analysis in Tokyo.

Contribution

The paper develops the SF-UQR model integrating spatial dependence into unconditional quantile regression using eigenvector spatial filtering, with an efficient estimation method and practical implementation in R.

Findings

SF-UQR effectively captures spatial dependence in quantile regression.

The model performs well in hedonic land price analysis in Tokyo.

Implementation is available in the R package 'spmoran'.

Abstract

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."