The Knowledge Graphs for Macroeconomic Analysis with Alternative Big Data
There are many known relationships among macroeconomic variables in economics, while some of them are even presented as “laws” — for example, money supply and inflation or benchmark interest rates and inflation. However, the well-known economic models usually utilize only a small amount of variables. Nowadays, with the advances in machine learning and big data fields, these established models might be improved. A possible solution is presented in the research paper of Yang et al. (2020). The authors construct knowledge graphs where they connect widely recognized variables such as GDP, inflation, etc., with other more or less known variables based on the massive textual data from financial journals and research reports published by leading think tanks, consulting firms or asset management companies. With the help of advanced natural language processing, it is possible to basically “read “all the relevant published research and find the relationships among the macroeconomic variables. While this task could take years for human readers, the machine learning method can go through these texts in a much shorter time.
Moreover, the usage of the knowledge graphs is shown in a forecasting problem of either inflation or investment. As the paper shows, the knowledge graphs could be utilized for variable selection with the ability to find alternative and novel relationships. Lastly, the forecasts based on knowledge graphs are also compared to the more traditional “baseline “approach, and authors compare the methods on both short and long-term forecasting periods.