Nonparametric prediction with spatial data

Abhimanyu Gupta and Javier Hidalgo

Papers from arXiv.org

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.

Date: 2020-08, Revised 2021-11
New Economics Papers: this item is included in nep-ecm and nep-ure
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Citations: View citations in EconPapers (1)

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http://arxiv.org/pdf/2008.04269 Latest version (application/pdf)

Related works:
Working Paper: Nonparametric prediction with spatial data (2022)
Working Paper: Nonparametric prediction with spatial data (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2008.04269

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