long - run macroeconomic welfare. therefore, most central banks – like the ecb and the bundesbank before it – are committed to the primary objective of achieving and maintaining price stability. in order to be able to assess the risks to price stability, monetary policy makers need models which represent the relevant structural relationships between the price level, the monetary policy instruments and other factors affecting price movements like changes in oil prices or exchange rates. however, the exact nature of these relationships is surrounded by a high degree of uncertainty. as a consequence, the question of which relationships are the “ relevant ” ones and which simplifications are “ adequate ” is – and will probably always be - a contentious one. one of the lessons that we have learned from recent research is that focusing on specification errors or parameter uncertainty in the neighbourhood of a particular reference model may dramatically understate the true degree of model uncertainty. it is therefore advisable for central banks to base their decisions on a broad range of models which reflect various assumptions regarding the transmission process. in fact, this is the approach which most central banks – and certainly the ecb – are already practising. “ wer gar zu viel bedenkt, wird wenig leisten ” aus : wilhelm tell. siehe greenspan, alan : monetary policy under uncertainty, rede auf dem symposium der federal reserve bank of kansas city, jackson hole, wyoming, 29. august 2003, sowie greenspan, alan : risks and uncertainty in monetary policy, rede auf dem treffen der american economic association, san diego, california, am 3. januar 2004. now, the question is which weights should policy makers attach to each of these competing models when assessing the risks to price stability? this is a very controversial issue. part of the academic literature assumes that policymakers or their staff form priors over the models and choose the approach which minimises average expected loss ( bayesian approach ). in contrast, the robust control approach assumes that policymakers are completely agnostic with respect to the probability that a specific model may represent the actual economy and thus choose the approach which minimises the maximum loss across all models ( minimax preferences ). in order to implement the bayesian approach, policymakers need to be able to attach a specific probability weight to each of the competing models. while this may be a very difficult task, the minimax approach also suffers from several drawbacks. 3 one fundamental problem

claudia buch : evaluation and macroprudential policy speech by prof claudia buch, deputy president of the deutsche bundesbank, at the annual meeting of the verein fur socialpolitik, munster, 7 september 2015. * * * ladies and gentlemen the theme of this year ’ s annual meeting of the verein fur socialpolitik is “ economic development – theory and policy ”. the evaluation of development aid projects has a relatively long tradition – from which many other policy areas may benefit. much greater use could still be made of research findings on the evaluation of the efficiency and effectiveness of public projects and economic policy measures. i wish to illustrate this using macroprudential policy as an example. why are development aid projects frequently evaluated? for one thing, financiers expect accountability on the impact of their financial assistance. for another, researchers can often contribute to the project design. that lets them incorporate the features needed for an ex post impact analysis while the project is still on the drawing board. ex ante planning allows evaluation data to be collected in a manner conducive to maximising the reliability of econometric identification. tackling a stimulating research topic without nagging questions about the identification strategy boosts the likelihood of being published. that ’ s an incentive for researchers to get involved in evaluation and the key to putting evidence - based economic policymaking into practice. and that, ladies and gentlemen, is what i think we need to see more of in other policy fields. but the actual impact of measures is often just as uncertain outside the realm of development aid. in many cases, the projects in question are much larger still and relate to entire countries – tax policy, education policy, health policy – and not forgetting financial market regulation. the question for me is this : how can we make sure that these areas, too, are subject to greater and, above all, scientifically proven evaluation? there are two hurdles to overcome, in my view, but neither of them are insurmountable. the first is the inadequate data pool, the second a frequent absence of a need for the scientifically - based evaluation of policy measures. as deputy president of the bundesbank, one of my areas of responsibility is financial stability. this includes macroprudential policy. we still don ’ t know all that much about the impact of macroprudential policy measures right now. the available evidence is very sparse ; it is often no more than cross -