this year, down from 8. 7 % in 2022. however, it is likely to remain at levels that are well above 2 % in both 2024 and 2025, at 3. 1 % and 2. 7 %, respectively. what is more, if we look below the surface, inflation is proving to be more persistent than expected. the core inflation rate, which excludes changes in energy and food prices, will probably come to 5. 2 % this year – that's actually even higher than the rate of 3. 9 % recorded last year. the sharp increases in energy and food prices, then, are being transmitted through to the broader basket of goods. only in the coming years do we expect to see core inflation weaken as well, with core inflation projected to drop to 3. 1 % in 2024 and 2. 8 % in 2025. the path of monetary policy is influenced by events in the euro area. that is why i would also like to briefly outline the current state of play in the euro area. 3 / 7 bis - central bankers'speeches according to their june forecast, eurosystem staff are expecting to see growth of 0. 9 % in 2023. the euro area as a whole, then, is faring noticeably better than germany. eurosystem experts are expecting euro area growth to pick up in 2024 and 2025, with annual growth rates of 1. 5 % in 2024 and 1. 6 % in 2025. the euro area's inflation rate is moving much like germany's, with headline inflation projected to fall from 8. 4 % in 2022 to 5. 3 % this year, and then to 3. 0 % in 2024 and 2. 2 % in 2025. however, the high level of core inflation remains a cause for concern in the euro area, too. eurosystem staff are currently expecting core inflation to rise on an annual average from 3. 9 % in 2022 to 5. 1 % this year, and then to 3. 0 % in 2024 and 2. 3 % in 2025. there's one point i would like to emphasise at this juncture : our experts are now expecting somewhat higher inflation rates over the projection horizon as a whole than in the previous forecast from march. and even towards the end of 2025, they do not foresee the inflation rate falling to our medium - term target of 2 %. as i see it, this illustrates

monitored time series. the suite - of - models approach has the advantage that it takes into account model uncertainty to some extent and does not neglect potentially important information. as for the individual design of a central bank ’ s forecast model, it may differ with respect to variable coverage, econometric rigour and the economic theory it incorporates. basically, however, macroeconomic variables can be predicted in two different ways : structural macroeconometric models, which include traditional multi - equation models and dsge models, and short - term forecast models, usually based on time - series analysis. although structural macroeconometric models remain workhorses used for forecasting and policy simulations, short - term forecast models have received increasing attention in the eurosystem in recent years. why is the short - term horizon so important? for the reasons i have already mentioned, monetary policy decisions require timely information on the current state of the economy. such information is not always available, however. gdp as an important indicator of macroeconomic activity is, for instance, released with a considerable time delay and only once a quarter. in germany, gdp is released about five to six weeks after the end of the reference quarter. for example, we expect the federal statistical office to release first - quarter gdp data on 15 may 2009. to allow an assessment of developments in real activity in the current quarter, the central bank ’ s economists must therefore provide a so - called “ nowcast ”. hundreds of variables including high - frequency indicators are, in principle, available for this purpose. think, for instance, of financial or survey data. but as policy - makers pursue a full - information strategy, central bankers would like to sum up the ( at times conflicting ) information content of all these variables into a single measure. the workhorse models ( structural multiple - equation models and dsge models ) are based on quarterly data only and are therefore ill - suited to evaluate the vast amount of highfrequency data available. by contrast, modern short - term forecast models are well able to process large datasets. what is more, they can also deal with datasets that are unbalanced because data is sampled at different frequencies ( monthly – quarterly ) or is subject to different publication lags, leading to missing observations at the end of the sample ( the “ ragged edge ” of multivariate data ). the most prominent recent examples of such short - term models are : large factor models, mixed - frequency state -