arXiv 2 May 2023 · Econometrics · publishedComputational Economics (2024) · 2 citations (OpenAlex)
arXiv:2305.01201 · PDF · DOI · OpenAlex · Extracted main text
An input-output table is an important data for analyzing the economic situation of a region. Generally, the input-output table for each region (regional input-output table) in Japan is not always publicly available, so it is necessary to estimate the table. In particular, various methods have been developed for estimating input coefficients, which are an important part of the input-output table. Currently, non-survey methods are often used to estimate input coefficients because they require less data and computation, but these methods have some problems, such as discarding information and requiring additional data for estimation. In this study, the input coefficients are estimated by approximating the generation process with an artificial neural network (ANN) to mitigate the problems of the non-survey methods and to estimate the input coefficients with higher precision. To avoid over-fitting due to the small data used, data augmentation, called mixup, is introduced to increase the data size by generating virtual regions through region composition and scaling. By comparing the estimates of the input coefficients with those of Japan as a whole, it is shown that the accuracy of the method of this research is higher and more stable than that of the conventional non-survey methods. In addition, the estimated input coefficients for the three cities in Japan are generally close to the published values for each city.
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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 | Zhang2018 APACrefauthors Zhang, H. , Cisse, M. , Dauphin, Y.N. Lopez… (2018) 2018 | 0.874 | 5 | 2 | 100% |
| 2 | Papadas2002 APACrefauthors Papadas, C.T. \ Hutchinson, W.G. APACrefa… (2002) 2002 | 0.737 | 3 | 2 | 100% |
| 3 | Flegg2021 APACrefauthors Flegg, A.T. , Lamonica, G.R. , Chelli, F.M.… (2021) 2021 | 0.644 | 2 | 2 | 100% |
| 4 | Wu2020 APACrefauthors Wu, S. , Zhang, H.R. , Valiant, G. Ré, C. APAC… (2020) 2020 | 0.644 | 2 | 2 | 100% |
| 5 | Abbasimehr2020 APACrefauthors Abbasimehr, H. , Shabani, M. Mohsen, Y… (2020) 2020 | 0.405 | 1 | 1 | 100% |
| 6 | Bacharach1970 APACrefauthors Bacharach, M. APACrefauthors \ (1970) 1970 | 0.405 | 1 | 1 | 100% |
| 7 | Bonfiglio2008 APACrefauthors Bonfiglio, A. \ Francesco, C. APACrefau… (2008) 2008 | 0.405 | 1 | 1 | 100% |
| 8 | Botev2017 APACrefauthors Botev, A. , Lever, G. Barber, D. APACrefaut… (2017) 2017 | 0.405 | 1 | 1 | 100% |
| 9 | Chapelle2000 APACrefauthors Chapelle, O. , Weston, J. , Bottou, L. V… (2000) 2000 | 0.405 | 1 | 1 | 100% |
| 10 | Dao2019 APACrefauthors Dao, T. , Gu, A. , Ratner, A.J. , Virginia, S… 2019 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Enhancing the Accuracy of Regional Input–Output Table Estimation: A Deep Learning Approach | 0.874 | 10 | 2 |