Economic agents and financial authorities require frequent updates to a path of accurate inflation forecasts and need forecasts to include an explanation of the factors by which they are determined. This paper studies how to approach this need, developing a method for analysing inflation in the euro area, measured according to HICP. Time series models using the most recent information on prices and an important functional and geographically disaggregation can provide monthly forecasts which are reasonably accurate, but they do not provide an explanation of the factors by which the forecast is determined. In this respect, it is important to enlarge the data set used considering explanatory variables and build congruent econometric models including variables which, following previous works by D. Hendry, capture disequilibria on different markets, goods and services, labour, monetary and international. The final result of this work shows that combining the forecasts from a monthly time series vector model, constructed on price subindexes from a disaggregation of the HICP by countries and sectors, with the forecasts derived from a quarterly econometric vector model on aggregate inflation and other economic variables, very accurate forecasts are obtained. Both vector models are specified including empirical cointegration restrictions, which in the first case capture the constrains necessary present between the trends of the price subindexes and in the second approximate the long-run restrictions postulated by economic theory.