For unstructured data, the study uses Naver search data as a proxy for public sentiment. Using Dynamic Model Averaging and Selection (DMA and DMS) techniques, it incorporates eight Naver search indices alongside traditional macroeconomic variables. The findings suggest that online search data improves predictive power, especially in capturing economic turning points.

The study also compares these big data-driven models with a Dynamic Stochastic General Equilibrium (DSGE) model. While DSGE offers policy analysis capabilities, its in-sample forecasts make direct comparison difficult. However, DMA and DMS models using search indices seem to better capture the GDP plunge in 2020.

Based on the research findings, the author offers several suggestions to maximize the potential of big data. He stresses the importance of discovering and constructing diverse data sources, while also developing new analytical techniques such as machine learning. Furthermore, he suggests that big data models can be used as auxiliary indicators to complement existing forecasting models, and proposes that combining structural models with big data methodologies could create synergistic effects. Lastly, by using text mining on various online sources to build comprehensive databases, we can secure richer and more real-time economic data. These suggestions demonstrate the significant potential of big data in improving the accuracy of international macroeconomic forecasting, particularly emphasizing its effectiveness in situations where the economy is undergoing rapid changes.