NONPARAMETRIC PREDICTION WITH SPATIAL DATA

Abhimanyu Gupta and Javier Hidalgo

Econometric Theory, 2023, vol. 39, issue 5, 950-988

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: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:39:y:2023:i:5:p:950-988_3

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