#2765. Application of machine-learning models to estimate regional input coefficients and multipliers

November 2026publication date
Proposal available till 30-05-2025
4 total number of authors per manuscript3510 $

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Journal’s subject area:
Economics, Econometrics and Finance (all);
Statistics, Probability and Uncertainty;
Geography, Planning and Development;
Earth and Planetary Sciences (miscellaneous);
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Abstract:
The article proposes a new approach to estimating regional input coefficients. It uses the power of machine learning (ML) algorithms to estimate the regional input coefficients of one region based on the IOT of several other regions for which reliable data is available. The results show superior performance of ML models compared to location-based models.
Keywords:
input coefficients; input–output tables; location quotient; machine learning; regionalization

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