The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data
This work addresses the challenge of handling many variables in macroeconomic analysis for economists and policymakers, but it is incremental as it builds on existing big data and knowledge graph approaches.
The authors tackled the problem of macroeconomic forecasting by constructing a knowledge graph that integrates traditional economic variables with alternative big data variables extracted from textual sources using NLP. They demonstrated that using this knowledge graph for variable selection significantly improves forecasting accuracy, particularly for long-run forecasts, compared to statistical methods.
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.