more reliable, data. cautious central bankers only change the status quo in monetary policy when the signs of an assumed change in the development of key macroeconomic variables become more apparent. the problem of data uncertainty suggests that we should not rely on one single indicator when we have to assess risks to price stability. instead we should analyse a broad range of information variables. however, when following such a broad approach, we have to take into account that the extent of measurement problems differs from one indicator to the other. very generally speaking, and that ’ s my third point, i ’ d like to emphasise that indicators from the monetary and financial sphere are far less affected by the problem of data uncertainty than real economic variables such as output and the output gap. against this background, it comes as no surprise that a monetary policy approach should be as robust as possible against different forms of data and model uncertainties. in the past few years, a number of simulation studies have been carried out to assess which monetary policy approach achieves good results under various assumptions about the transmission process. these studies take into account model uncertainty. however, they largely disregard the problem of data uncertainty. consequently, their value for practical monetary policy is rather limited. i think, the search for a robust strategy in monetary policy could be broadened to include the kind of measurement problems that we will discuss here at this conference. taking this broader perspective, and that ’ s my fourth and final point, a monetary policy would be considered robust if it performed well in the face of different forms of data and model uncertainty. research in this direction has only just begun. 1 i hope that this research will give further support to the ecb ’ s strategy. let me conclude by emphasising that research on real - time data is of utmost importance for central banks. that ’ s why i am very much interested in the papers that you will present here today and tomorrow. i wish you thought - provoking discussions and a pleasant stay at our conference facilities. thank you very much for your attention. see, for instance, walsh, c ( 2003 ) : “ implications of a changing economic structure for the strategy of monetary policy ”, federal reserve bank of kansas city ”.

hermann remsperger : real - time data and monetary policy welcome address by professor hermann remsperger, member of the executive board of the deutsche bundesbank, at the bundesbank conference on “ real - time data and monetary policy ”, eltville, 28 may 2004. * * * ladies and gentlemen, i have the pleasure of welcoming you to our conference on “ real - time data and monetary policy ” which has been organised by heinz herrmann, athanasios orphanides and pierre siklos. our conference concentrates on the uncertainty about the actual level of key macro variables. until a few years ago, this issue received little attention in the academic debate on monetary policy. this has changed dramatically over the last few years, not least under the influence of economists attending this conference like athanasios orphanides or dean croushore. awareness of the measurement problems in real - time data has important consequences. one could separate them into two classes which i would like to term “ ex post ” aspects on the one side and “ ex ante ” aspects on the other. with “ ex post ” aspects, i mean the implications for the empirical analysis of monetary policy. for instance, we have learned that the evaluation of forecasts based on final data may be misleading because it does not take into account which data were actually available at the time the forecasts were made. or, as another example, when analysing monetary policy decisions, it is important to reconstruct the information set available to policy makers at the time the decisions were made. with “ ex ante ” aspects, i mean the implications of measurement problems for the conduct of monetary policy. this category includes questions like “ how can we learn from past data revisions to improve our knowledge about future data? ” or “ how will the awareness of these measurement problems influence our policy decisions and the choice of our monetary policy approach? ”. obviously, all these topics are interlinked. answers to these questions require real - time data sets for key variables like real and nominal gdp, potential output and inflation. at first glance, developing a real - time data set seems to be rather simple - just enter old data into spreadsheets and save them for future use. but in practice, old data are usually overwritten by new data without saving the older vintage. producing a real - time data set thus requires a substantial amount of effort, including digging through old source data and figuring out what data were