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

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  • Amir Hossein Pakizeh
  • 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.

Suggested Citation

  • Amir Hossein Pakizeh & Hamed Kashani, 2022. "Application of machine-learning models to estimate regional input coefficients and multipliers," Spatial Economic Analysis, Taylor & Francis Journals, vol. 17(2), pages 178-205, April.
    • Handle: RePEc:taf:specan:v:17:y:2022:i:2:p:178-205
    DOI: 10.1080/17421772.2021.1959046
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    Cited by:

    1. Sargento, Ana Lúcia Marto & Lahr, Michael L. & Ferreira, João Pedro & de la Torre Cuevas, Fernando, 2024. "Revisiting methods for estimating interregional input-output accounts: It's not just about trade flows," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 664-677.

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