SummaryThis paper develops a factor model for forecasting inflation in the euro area. The model can handle variables with different timeliness, sample size and frequency. We show that the forecasts based on the factor model outperform naïve random walk forecasts, a hard to beat benchmark for euro area inflation forecasts in recent years, at horizons of and beyond nine months ahead. They are also comparable, in terms of accuracy, to the judgemental forecasts prepared in the context of the Eurosystem macroeconomic projection exercises. The factor model is therefore a very suitable tool to extract the signal on current and future euro area inflation from new data releases.

The sovereign debt crisis has increased the importance of monitoring budgetary execution. We employ real‐time data using a mixed data sampling (MiDaS) methodology to demonstrate how budgetary slippages can be detected early on. We show that in spite of using real‐time data, the year‐end forecast errors diminish significantly when incorporating intra‐annual information. Our results show the benefits of forecasting aggregates via subcomponents, in this case total government revenue and expenditure. Our methodology could significantly improve fiscal surveillance and could therefore be an important part of the European Commission's model toolkit.

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