Using the web to predict regional trade flows: data extraction, modelling, and validation

Emmanouil Tranos, Andre Carrascal Incera and George Willis

No 9bu5z, OSF Preprints from Center for Open Science

Abstract: Despite the importance of interregional trade for building effective regional economic policies, there is very little hard data to illustrate such interdependencies. We propose here a novel research framework to predict interregional trade flows by utilising freely available web data and machine learning algorithms. Specifically, we extract hyperlinks between archived websites in the UK and we aggregate these data to create an interregional network of hyperlinks between geolocated and commercial webpages over time. We also use some 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 illustrative 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 sub-regional 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.

Date: 2022-07-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-geo, nep-int and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://osf.io/download/62c552d37ddff526409a6527/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:9bu5z

DOI: 10.31219/osf.io/9bu5z

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().