Description

This paper investigates whether artificial intelligence techniques—encompassing both machine learning and deep learning models—can enhance the accuracy of now-casts for Chile’s monthly economic activity index (IMACEC). The analysis relies on a large and diverse real-time dataset that includes both traditional macroeco-nomic variables and high-frequency monthly administrative data (from electronic tax records). Three main findings emerge. First, nonlinear models—particularly XGBoost—achieve the lowest root mean squared errors, whereas linear regularized approaches such as SVR and LASSO also show competitive performance. This highlights the value of flexible nonlinear methods and regularized linear approaches when dealing with heterogeneous data. Second, features derived from electronic tax records—such as trade credit volumes and sectoral sales by region—consistently rank among the most important predictors across models. Third, the strongest-performing models—XGBoost, SVR, and LASSO—achieve lower errors than tra-ditional econometric benchmarks, which rely solely on standard macroeconomic aggregates and exclude non-traditional datasets. Overall, the findings show that timely administrative data, combined with AI approaches, can significantly improve economic surveillance and decision-making.