Convolutional regression for big spatial data

Yasumasa Matsuda and Xin Yuan

No 124, DSSR Discussion Papers from Graduate School of Economics and Management, Tohoku University

Abstract: Recently it is common to collect big spatial data on a national or continental scale at discrete time points. This paper aims at a regression model when both dependent and independent variables are big spatial data. Regarding spatial data as functions over a region, we propose a functional regression by a parametric convolution kernel together with the least squares estimation on the frequency domain by applying Fourier transform. It can handle massive datasets with asymptotic validations under the mixed asymptotics. The regression is applied to Covid-19 weekly new cases and human mobility collected in city levels all over Japan to find that an increase of human mobility is followed by an increase of Covid-19 new cases in time lag of two weeks.

Pages: 31 pages
Date: 2022-02
New Economics Papers: this item is included in nep-ecm, nep-tre and nep-ure
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http://hdl.handle.net/10097/00133836

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Persistent link: https://EconPapers.repec.org/RePEc:toh:dssraa:124

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