This thesis develops a hybrid factor based framework for nowcasting Brazil s Industrial Production (IP), combining market expectations and high-frequency macroeconomic indicators. A Dynamic Factor Model (DFM) is first estimated using variables grouped by economic reasoning across real activity, surveys, and trade. The extracted factors are then used in bias-corrected regressions alongside the Bloomberg Consensus forecast and electricity consumption data from the national grid operator (ONS). This hybrid specification delivers significant outof-sample gains over the consensus, improving predictive accuracy by more than 15 percentage in RMSE and 17 percentage in R2 OOS during the post pandemic period (2021–2024). The robustness of the result is supported by Diebold–Mariano and Clark–West tests. Other tested approaches, including sentiment based and purely statistical models, did not yield additional improvements. Overall, the findings highlight the value of combining judgment based forecasts with model derived information for nowcasting macroeconomic variables in Brazil.