Colombia · Bogotá · See the Colombia profile · See the Bogotá profile
Evidence: Quasi-experimental Top 74% 47/100 · Ask Evidence Copilot about this practice
Banco de la República's July 2025 paper shows a Random Forest model combining Google Trends data with macro indicators nowcasts Colombian monthly inflation as accurately as the bank's analyst survey — about 1.5 weeks earlier — beating SARIMA, Ridge and Lasso benchmarks.
Colombia's official monthly inflation figure is released with a lag, and Banco de la República's own Monthly Survey of Financial Analysts (MES) takes time to compile, leaving policymakers without an early read on price pressures between releases.
In Borradores de Economía No. 1318 (July 2025), economists Felipe Roldán-Ferrín and Julián Andrés Parra-Polanía built RF-GT, a Random Forest machine-learning model trained on historical inflation data, macroeconomic indicators and Google Trends web-search activity, with hyperparameters tuned through time-series cross-validation.
The model's out-of-sample performance was tested against SARIMA, Ridge and Lasso statistical benchmarks and against the bank's own MES analyst-survey median forecast over the 2023-2024 period.
RF-GT consistently outperformed the SARIMA, Ridge and Lasso benchmarks and matched the accuracy of the bank's own MES analyst-survey median forecast — while producing its estimate roughly one and a half weeks earlier than the survey is available.
This remains a research working paper from the bank's economics department rather than a confirmed input to the formal monetary-policy decision process, and its out-of-sample track record covers only a two-year window.
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