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Application of machine-learning models to estimate regional input coefficients and multipliers

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https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F17421772.2021.1959046;h=repec:taf:specan:v:17:y:2022:i:2:p:178-205
Time Added
6/12/2022, 6:39:00 PM
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Machine Learning
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Authors
Amir Hossein Pakizeh and Hamed Kashani
Abstract
Due to the unavailability of accurate data and the limitations of the existing methods reliable input–output tables (IOTs) may not be available for all the regions in a country. This study proposes a novel approach to estimate the regional input coefficients. It harnesses the capabilities of machine-learning (ML) algorithms to estimate the regional input coefficients of one region based on the IOTs of multiple other regions for which reliable data are available. The application of three ML algorithms is investigated using data from Japan. The results highlight the superior performance of the ML models compared with location quotient models.
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Year Published
2022
Series
Spatial Economic Analysis 2022 vol. 17 issue 2 178-205
Rank
0.8
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