Using the Web to Predict Regional Trade Flows: Data Extraction, Modeling, and Validation. (16th March 2023)

• Record Type: Journal Article
• Authors: Tranos, Emmanouil; Carrascal-Incera, André; Willis, George
• Is Part Of: Annals of the American Association of Geographers. Volume 113:Number 3(2023)
• Page Start: 717
• Page End: 739
• Publication Date: 2023-03-16
• Subjects: interregional trade -- machine learning -- prediction -- random forest -- Web archives -- Web data; 区域间贸易 -- 机器学习 -- 预测 -- 随机森林 -- 网站存档 -- 网络数据。; aprendizaje automático -- archivos web -- bosque aleatorio -- comercio interregional -- datos web; Geography -- Periodicals; Environmental sciences -- Periodicals; Geography; Electronic journals; Periodicals; 550
• DOI: 10.1080/24694452.2022.2109577
• Languages: English
• ISSNs: 2469-4452

Abstract

Abstract : Despite the importance of interregional trade for building effective regional economic policies, there are very few hard data to illustrate such interdependencies. We propose here a novel research framework to predict interregional trade flows by utilizing freely available Web data and machine learning algorithms. Specifically, we extract hyperlinks between archived Websites in the United Kingdom and we aggregate these data to create an interregional network of hyperlinks between geolocated and commercial Web pages over time. We also use existing interregional trade data to train our models using random forests and then make out-of-sample predictions of interregional trade flows using a rolling-forecasting framework. Our models illustrate great predictive capability with R 2 greater than 0.9. We are also able to disaggregate our predictions in terms of industrial sectors, but also at a subregional level, for which trade data are not available. In total, our models provide a proof of concept that the digital traces left behind by physical trade can help us capture such economic activities at a more granular level and, consequently, inform regional policies.