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Spatially filtered unconditional quantile regression: Application to a hedonic analysis

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  • D. Murakami
  • H. Seya

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.”

Suggested Citation

  • D. Murakami & H. Seya, 2019. "Spatially filtered unconditional quantile regression: Application to a hedonic analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 30(5), August.
    • Handle: RePEc:wly:envmet:v:30:y:2019:i:5:n:e2556
    DOI: 10.1002/env.2556
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    Cited by:

    1. Paul Harris & Bruno Lanfranco & Binbin Lu & Alexis Comber, 2020. "Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay," Agriculture, MDPI, vol. 10(7), pages 1-17, July.
    2. Sophie Häse & Georg Hirte, 2023. "The impact of unexpected flood events and adaption measures on lot prices [Die Auswirkungen von unerwarteten Hochwasserereignissen und Anpassungsmaßnahmen auf Grundstückspreise]," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 43(1), pages 29-68, April.
    3. Yu, Jisang & Sumner, Daniel A. & Lee, Hyunok, 2021. "Premium rates and selection in specialty crop insurance markets: Evidence from the catastrophic coverage participation," Food Policy, Elsevier, vol. 101(C).

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