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Nonparametric prediction with spatial data

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  • Abhimanyu Gupta
  • Javier Hidalgo

Abstract

We describe a (nonparametric) prediction algorithm for spatial data, based on a canonical factorization of the spectral density function. We provide theoretical results showing that the predictor has desirable asymptotic properties. Finite sample performance is assessed in a Monte Carlo study that also compares our algorithm to a rival nonparametric method based on the infinite AR representation of the dynamics of the data. Finally, we apply our methodology to predict house prices in Los Angeles.

Suggested Citation

  • Abhimanyu Gupta & Javier Hidalgo, 2022. "Nonparametric prediction with spatial data," STICERD - Econometrics Paper Series 621, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:621
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    References listed on IDEAS

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    Cited by:

    1. S.‐H. Arnaud Kanga & Ouagnina Hili & Sophie Dabo‐Niang & Assi N'Guessan, 2023. "Asymptotic properties of nonparametric quantile estimation with spatial dependency," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(3), pages 254-283, August.

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    More about this item

    Keywords

    Lattice data; unilateral models; canonical factorization; spectral density; nonparametric prediction;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

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