Nonparametric prediction with spatial data

Abhimanyu Gupta and Javier Hidalgo

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

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.

Keywords: Lattice data; unilateral models; canonical factorization; spectral density; nonparametric prediction (search for similar items in EconPapers)
Date: 2022-01
New Economics Papers: this item is included in nep-geo, nep-ore and nep-ure
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https://sticerd.lse.ac.uk/dps/em/em621.pdf (application/pdf)

Related works:
Working Paper: Nonparametric prediction with spatial data (2022)
Working Paper: Nonparametric prediction with spatial data (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:621

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