In this paper, we address whether using a disaggregated series or combining an aggregated and disaggregated series improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques, such as the weighted lag adaptive least absolute...

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data from IPCA over twelve months ahead. We used IPCA in a monthly basis, over the period...

Our paper aims to evaluate two novel methods on selecting the best forecasting model or its combination based on a Machine Learning approach. The methods are based on the selection of the "best" model, or combination of models, by cross-validation technique, from a set of possible models. The...

The seminal study of Meese and Rogoff (1983) on exchange rate forecastability had a great impact on the international finance literature. The authors showed that exchange rate forecasts based on structural models are worse than a naive random walk. This result is known as the Meese-Rogoff (MR)...

Economic forecasting during structural breaks is challenging due to the possible systematic failure of existent models. Robust forecast devices are able to provide unbiased forecasts just after structural change but at the cost of higher variance in normal times. Reinforcement learning (RL)...