Nonparametric prediction with spatial data

Abhimanyu Gupta and Javier Hidalgo

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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: STICERD; ES/R006032/1 (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2022-05-23
New Economics Papers: this item is included in nep-for, nep-geo and nep-ure
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Published in Econometric Theory, 23, May, 2022. ISSN: 0266-4666

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http://eprints.lse.ac.uk/115292/ Open access version. (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:ehl:lserod:115292

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