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

TL;DR

This paper introduces a deep learning approach to improve the accuracy of regional input-output table estimation, addressing limitations of traditional non-survey methods and demonstrating superior performance on Japanese data.

Contribution

The study develops a novel deep learning method combined with matrix balancing for more accurate regional input-output table estimation, surpassing traditional approaches.

Findings

Higher estimation accuracy than matrix balancing under ideal assumptions

Effective use of deep learning on regional economic data

Potential for more precise regional economic analysis

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.