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An input-output table is an important data for analyzing the economicsituation of a region. Generally, the input-output table for each region(regional input-output table) in Japan is not always publicly available, so itis necessary to estimate the table. In particular, various methods have beendeveloped for estimating input coefficients, which are an important part of theinput-output table. Currently, non-survey methods are often used to estimateinput coefficients because they require less data and computation, but thesemethods have some problems, such as discarding information and requiringadditional data for estimation.  In this study, the input coefficients are estimated by approximating thegeneration process with an artificial neural network (ANN) to mitigate theproblems of the non-survey methods and to estimate the input coefficients withhigher precision. To avoid over-fitting due to the small data used, dataaugmentation, called mixup, is introduced to increase the data size bygenerating virtual regions through region composition and scaling.  By comparing the estimates of the input coefficients with those of Japan as awhole, it is shown that the accuracy of the method of this research is higherand more stable than that of the conventional non-survey methods. In addition,the estimated input coefficients for the three cities in Japan are generallyclose to the published values for each city.

Estimating Input Coefficients for Regional Input-Output Tables Using Deep Learning with Mixup