The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data

TL;DR

This paper introduces a knowledge graph integrating traditional macroeconomic variables with new big data variables, extracted via NLP from textual data, to improve economic forecasting accuracy.

Contribution

It presents a novel knowledge graph that combines traditional and big data variables for macroeconomic analysis, enhancing forecasting methods.

Findings

KG-based variable selection outperforms statistical methods in accuracy

Significant improvement in long-term economic forecasts

NLP effectively extracts relevant variables from textual data

Abstract

The current knowledge system of macroeconomics is built on interactions among a small number of variables, since traditional macroeconomic models can mostly handle a handful of inputs. Recent work using big data suggests that a much larger number of variables are active in driving the dynamics of the aggregate economy. In this paper, we introduce a knowledge graph (KG) that consists of not only linkages between traditional economic variables but also new alternative big data variables. We extract these new variables and the linkages by applying advanced natural language processing (NLP) tools on the massive textual data of academic literature and research reports. As one example of the potential applications, we use it as the prior knowledge to select variables for economic forecasting models in macroeconomics. Compared to statistical variable selection methods, KG-based methods achieve significantly higher forecasting accuracy, especially for long run forecasts.