This paper proposes a methodology for now-casting and forecasting inflation using data with a sampling frequency which is higher than monthly. The data are modeled as a trading day frequency factor model, with missing observations in a state space representation. For the estimation we adopt the methodology proposed by Bańbura and Modugno (2010). In contrast to other existing approaches, the methodology used in this paper has the advantage of modeling all data within a single unified framework which allows one to disentangle the model-based news from each data release and subsequently to assess its impact on the forecast revision. The results show that the inclusion of high frequency data on energy and raw material prices in our data set contributes considerably to the gradual improvement of the model performance. As long as these data sources are included in our data set, the inclusion of financial variables does not make any considerable improvement to the now-casting accuracy.
