A New Approach to Building the Interindustry Input--Output Table

10 Pages Posted: 6 Jun 2015 Last revised: 30 May 2016

Date Written: May 31, 2015

Abstract

We present a new approach to estimating the interdependence of industries in an economy by applying data science solutions. By exploiting interfirm buyer--seller network data, we show that the problem of estimating the interdependence of industries is similar to the problem of uncovering the latent block structure in network science literature. To estimate the underlying structure with greater accuracy, we propose an extension of the sparse block model that incorporates node textual information and an unbounded number of industries and interactions among them. The latter task is accomplished by extending the well-known Chinese restaurant process to two dimensions. Inference is based on collapsed Gibbs sampling, and the model is evaluated on both synthetic and real-world datasets. We show that the proposed model improves in predictive accuracy and successfully provides a satisfactory solution to the motivated problem. We also discuss issues that affect the future performance of this approach.

Keywords: Relational model, Bayesian nonparametrics, Topic Models, Network, Input-Output table

JEL Classification: C49; R15; D57; C11

Suggested Citation

Hisano, Ryohei, A New Approach to Building the Interindustry Input--Output Table (May 31, 2015). Available at SSRN: https://ssrn.com/abstract=2612567 or http://dx.doi.org/10.2139/ssrn.2612567

Ryohei Hisano (Contact Author)

University of Tokyo ( email )

Yayoi 1-1-1
Bunkyo-ku
Tokyo, Tokyo 113-8657
Japan

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