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The current knowledge system of macroeconomics is built on interactions amonga small number of variables, since traditional macroeconomic models can mostlyhandle a handful of inputs. Recent work using big data suggests that a muchlarger number of variables are active in driving the dynamics of the aggregateeconomy. In this paper, we introduce a knowledge graph (KG) that consists ofnot only linkages between traditional economic variables but also newalternative big data variables. We extract these new variables and the linkagesby applying advanced natural language processing (NLP) tools on the massivetextual data of academic literature and research reports. As one example of thepotential applications, we use it as the prior knowledge to select variablesfor economic forecasting models in macroeconomics. Compared to statisticalvariable selection methods, KG-based methods achieve significantly higherforecasting accuracy, especially for long run forecasts.

The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data