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Enhancing the Accuracy of Regional Input-Output Table Estimation: A Deep Learning Approach

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  • Shogo Fukui

Abstract

Non-survey methods have been developed and applied for estimating regional input-output tables. However, there is an ongoing debate about the assumptions necessary for these methods and their accuracy. To address these issues, this study presents a deep learning method for estimating regional input-output tables. First, the quantitative economic data for regions is augmented by linear combinations. Then, deep learning is performed on each item in the input-output table, treating these items as target variables. Finally, regional input-output tables are estimated through matrix balancing to the predicted values from the trained model. The estimation accuracy of this method is verified using the 2015 input-output table for Japan as a benchmark. Compared to matrix balancing under the ideal assumption of known row and column sums, our method generally demonstrates higher estimation accuracy. Thus, this method is anticipated to provide a foundation for deriving more precise estimates of regional input-output tables.

Suggested Citation

  • Shogo Fukui, 2026. "Enhancing the Accuracy of Regional Input-Output Table Estimation: A Deep Learning Approach," Papers 2603.13823, arXiv.org.
    • Handle: RePEc:arx:papers:2603.13823
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    References listed on IDEAS

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    1. Theo Junius & Jan Oosterhaven, 2003. "The Solution of Updating or Regionalizing a Matrix with both Positive and Negative Entries," Economic Systems Research, Taylor & Francis Journals, vol. 15(1), pages 87-96, March.
    2. Anthony T. Flegg & Guiseppe R. Lamonica & Francesco M. Chelli & Maria C. Recchioni & Timo Tohmo, 2021. "Correction to: A new approach to modelling the input–output structure of regional economies using non‑survey methods," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 10(1), pages 1-1, December.
    3. Anthony T. Flegg & Guiseppe R. Lamonica & Francesco M. Chelli & Maria C. Recchioni & Timo Tohmo, 2021. "A new approach to modelling the input–output structure of regional economies using non-survey methods," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 10(1), pages 1-31, December.
    4. Giuseppe Ricciardo Lamonica & Francesco Maria Chelli, 2018. "The performance of non‐survey techniques for constructing sub‐territorial input‐output tables," Papers in Regional Science, Wiley Blackwell, vol. 97(4), pages 1169-1202, November.
    5. Umed Temursho & Jan Oosterhaven & M. Alejandro Cardenete, 2021. "A multi-regional generalized RAS updating technique," Spatial Economic Analysis, Taylor & Francis Journals, vol. 16(3), pages 271-286, July.
    6. Manfred Lenzen & Daniel D. Moran & Arne Geschke & Keiichiro Kanemoto, 2014. "A Non-Sign-Preserving Ras Variant," Economic Systems Research, Taylor & Francis Journals, vol. 26(2), pages 197-208, June.
    7. McDougall, Robert A., 1999. "Entropy Theory and RAS are Friends," Working papers 283439, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    8. McDougall, Robert, 1999. "Entropy Theory and RAS are Friends," GTAP Working Papers 300, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
    9. Manfred Lenzen & Richard Wood & Blanca Gallego, 2007. "Some Comments on the GRAS Method," Economic Systems Research, Taylor & Francis Journals, vol. 19(4), pages 461-465.
    10. 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.
    11. Geoff Riddington & Hervey Gibson & John Anderson, 2006. "Comparison of Gravity Model, Survey and Location Quotient-based Local Area Tables and Multipliers," Regional Studies, Taylor & Francis Journals, vol. 40(9), pages 1069-1081.
    12. Andrea Bonfiglio & Francesco Chelli, 2008. "Assessing the Behaviour of Non-Survey Methods for Constructing Regional Input-Output Tables through a Monte Carlo Simulation," Economic Systems Research, Taylor & Francis Journals, vol. 20(3), pages 243-258.
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