Application of machine-learning models to estimate regional input coefficients and multipliers. Issue 2 (3rd April 2022)

• Record Type: Journal Article
• Authors: Pakizeh, Amir Hossein; Kashani, Hamed
• Is Part Of: Spatial economic analysis. Volume 17:Issue 2(2022)
• Page Start: 178
• Page End: 205
• Publication Date: 2022-04-03
• Subjects: machine learning -- input–output tables -- regionalization -- input coefficients -- location quotient; C67 -- D57 -- R15; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2021.1959046
• Languages: English
• ISSNs: 1742-1772

Abstract

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.