arXiv 14 Mar 2026 · Econometrics
arXiv:2603.13823 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Nobuhiro Hosoe (2014) Estimation errors in input–output tables and prediction errors in computable general equilibrium analysis | 0.928 | 4 | 3 | 100% |
| 2 | Shogo Fukui (2025) Estimating input coefficients for regional input–output tables using deep learning with mixup self | 0.874 | 10 | 2 | 100% |
| 3 | Theo Junius and Jan Oosterhaven (2003) The solution of updating or regionalizing a matrix with both positive and negative entries | 0.737 | 3 | 3 | 67% |
| 4 | Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz (2018) Mixup: Beyond empirical risk minimization | 0.644 | 2 | 2 | 100% |
| 5 | Manfred Lenzen, Richard Wood, and Blanca Gallego (2007) Some comments on the gras method | 0.511 | 2 | 2 | 50% |
| 6 | Ronald E. Miller and Peter D. Blair (2022) Input–Output Analysis | 0.511 | 2 | 1 | 100% |
| 7 | Michael Bacharach (1970) Biproportional Matrices & Input–Output Change | 0.405 | 1 | 1 | 100% |
| 8 | Andrea Bonfiglio and Chelli Francesco (2008) Assessing the behaviour of non-survey methods for constructing regional input–output tables through a monte carlo simulation | 0.405 | 1 | 1 | 100% |
| 9 | Luyang Chen, Markus Pelger, and Jason Zhu (2023) Deep learning in asset pricing | 0.405 | 1 | 1 | 100% |
| 10 | Guangyu Ding and Liangxi Qin (2020) Study on the prediction of stock price based on the associated network model of lstm | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 39 scored citations.