Macroeconomic forecasting with statistically validated knowledge graphs

TL;DR

This paper introduces a method using statistically validated knowledge graphs derived from newspaper narratives to improve macroeconomic forecasts, demonstrating enhanced predictive power and interpretability over benchmarks.

Contribution

It presents a novel approach to constructing theme-based knowledge graphs with backbone filtering, improving economic forecasting and interpretability.

Findings

Knowledge graph features improve industrial production forecasts.

Eigenvector centrality shifts better predict theme importance changes.

Themes like 'disease' and 'economic' are highly predictive.

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.