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NONPARAMETRIC PREDICTION WITH SPATIAL DATA

Published online by Cambridge University Press:  23 May 2022

Abhimanyu Gupta  and
Javier Hidalgo
Abhimanyu Gupta*
Affiliation:
University of Essex
Javier Hidalgo
Affiliation:
London School of Economics
*
Address correspondence to Abhimanyu Gupta, Department of Economics, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK; e-mail: a.gupta@essex.co.uk.
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Abstract

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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.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 39 , Issue 5 , October 2023 , pp. 950 - 988
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

Research of the first author was supported by the Economic and Social Research Council (ESRC) grant ES/R006032/1. Research of the second author was supported by STICERD, LSE.

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