Deep Learning Models in Central Bank Forecasting

This paper assesses whether pretrained time-series foundation models and locally trained deep neural networks can make a useful contribution to central bank forecasting in a small, data-constrained economy. We compare TimesFM, Chronos-2, Moirai 2.0, and several locally estimated architectures with conventional statistical and econometric benchmarks and Banco de Portugal official projections. The applications cover monthly inflation, direct quarterly macroeconomic forecasting, and mixed-frequency nowcasting. All headline exercises use historical-vintage information and final data as evaluation truth. Foundation models are generally more competitive than locally trained neural networks and, for selected targets and horizons, match or outperform simple benchmarks and official projections. These gains are not uniform. Official projections remain particularly strong when judgment, granular or advance information, and exceptional economic events matter, while persistent or irregular series can leave little scope for deep-learning improvements. Locally trained and native mixed-frequency models nevertheless add useful signals for selected real and labor-market aggregates. The results therefore favor using deep-learning forecasts as systematic, reproducible complements to official projections rather than as universal replacements.