노이즈 필터링과 충분차원축소를 이용한 비정형 경제 데이터 활용에 대한 연구

초록

Text indicators are increasingly valuable in economic forecasting, but are often hindered by noise and highdimensionality. This study aims to explore post-processing techniques, specifically noise filtering and dimen-sionality reduction, to normalize text indicators and enhance their utility through empirical analysis. Predictivetarget variables for the empirical analysis include monthly leading index cyclical variations, BSI (business surveyindex) All industry sales performance, BSI All industry sales outlook, as well as quarterly real GDP SA (sea-sonally adjusted) growth rate and real GDP YoY (year-on-year) growth rate. This study explores the Hodrickand Prescott filter, which is widely used in econometrics for noise filtering, and employs sufficient dimensionreduction, a nonparametric dimensionality reduction methodology, in conjunction with unstructured text data.The analysis results reveal that noise filtering of text indicators significantly improves predictive accuracy forboth monthly and quarterly variables, particularly when the dataset is large. Moreover, this study demonstratedthat applying dimensionality reduction further enhances predictive performance. These findings imply that post-processing techniques, such as noise filtering and dimensionality reduction, are crucial for enhancing the utilityof text indicators and can contribute to improving the accuracy of economic forecasts.