编制行业间投入产出表的一种新方法
A New Approach to Building the Interindustry Input--Output Table
摘要：
我们提出了一种新的方法，通过应用数据科学解决方案来估计经济中各行业的相互依赖性。通过利用公司间的买方-卖方网络数据，我们表明，估计行业的相互依赖性的问题是类似的问题，揭示网络科学文献中的潜在块结构。为了更准确地估计底层结构，我们提出了一个扩展的稀疏块模型，该模型包含节点文本信息和无限数量的行业及其相互作用。后一项任务是通过将著名的中国餐馆过程扩展到两个维度来完成的。推理是基于折叠吉布斯抽样，该模型的合成和真实世界的数据集进行评估。我们表明，该模型提高了预测精度，并成功地提供了一个令人满意的解决方案的动机问题。我们还讨论了影响这种方法的未来性能的问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
