Tilly, Sonja and Livan, Giacomo (2021) Macroeconomic forecasting with statistically validated knowledge graphs. Expert Systems With Applications, 186. ISSN 0957-4174
Full text not available from this repository.Abstract
This study leverages narrative from global newspapers to construct theme-based knowledge graphs about world events, demonstrating that features extracted from such graphs improve forecasts of industrial production in three large economies compared to a number of benchmarks. Our analysis relies on a filtering methodology that extracts “backbones” of statistically significant edges from large graph data sets. We find that changes in the eigenvector centrality of nodes in such backbones capture shifts in relative importance between different themes significantly better than graph similarity measures. We supplement our results with an interpretability analysis, showing that the theme categories “disease” and “economic” have the strongest predictive power during the time period that we consider. Our work serves as a blueprint for the construction of parsimonious – yet informative – theme-based knowledge graphs to monitor in real time the evolution of relevant phenomena in socio-economic systems.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: This section introduces GDELT as data source and outlines the filtering methodology that is used to isolate relevant signals. The GDELT Project is a research collaboration of Google Ideas, Google Cloud, Google and Google News, the Yahoo! Fellowship at Georgetown University, BBC Monitoring, the National Academies Keck Futures Program, Reed Elsevier’s LexisNexis Group, JSTOR, DTIC and the Internet Archive. The project monitors world newspapers from a variety of perspectives, identifying and extracting items such as themes, emotions, locations, organisations and events. GDELT version two incorporates real-time translation from 65 languages and is updated every 15 min ( GDELT Project, 2015 ). It is a public data set available on the Google Cloud Platform. Publisher Copyright: © 2021 Elsevier Ltd Copyright: Copyright 2021 Elsevier B.V., All rights reserved. |
| Divisions: | Systemic Risk Centre |
| Date Deposited: | 07 May 2024 09:03 |
| Last Modified: | 12 Dec 2024 02:39 |
| URI: | http://eprints.lse.ac.uk/id/eprint/122917 |
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