##ia are systematically related to economic fundamentals. modelling them, however, requires nonlinear economic frameworks. similarly, we might possibly need nonlinear techniques to answer the question how policy should deal with really large shocks. linear models may give sufficiently accurate answers for dealing with small to medium - sized shocks, but if economic relationships are essentially non - linear, then such models may make considerable prediction errors in a situation where large shocks carry the economy far away from a state characterised by “ average behaviour ”. for all these reasons, it seems important from a policy perspective to develop and employ non - linear modelling and estimation techniques. iv. concluding remarks to sum up, over the past decades, tremendous progress has been made towards making monetary policy more of a science. undoubtedly, this has contributed to enhancing the effectiveness of monetary policy. at the same time, there is still room – and indeed a need – to further advance the scientific elements of monetary policy - making by addressing a number of conceptual, methodological and empirical challenges. some of these challenges are a consequence of the nature of the economic system and the uncertainty surrounding its evolution over time. the economic environment is continuously changing and at fast pace as a result of the actions of economic agents. technological advances, financial innovations, the process of globalisation, even changing preferences, are influencing the structure and functioning of markets and the monetary policy transmission mechanism. monetary policymakers have to take decisions against the background of the evolving economic environment and then face a host of uncertainties. this makes our task challenging – but admittedly also more exciting. moreover, these observations have two important implications for monetary policy. first, the ability to combine “ science and art ” in a well - structured, balanced, prudent and effective way will always remain a key feature of the successful performance of monetary policy. second, in an environment of continuous change and considerable uncertainty, it is essential that central banks provide an anchor of stability and certainty : through their commitment to and delivery of price stability. this conclusion is directly linked to the purpose of this conference, to celebrate the bundesbank ’ s 50 - year - long unwavering dedication to its task of “ safeguarding the currency ” ( “ die wahung zu sichern ” ) as it is stated in the bundesbank act. i am confident that 50 years from now, on the occasion of the 100th anniversary of the bundesbank, a conference can be again be organised to celebrate this great achievement : “ stable money for germany and

##geneity of agents and expectations. iii. 3. some modelling and methodological challenges this brings me to some key modelling and methodological challenges that deserve more attention in order to enhance the analytical tools for monetary policy : • first, how to develop models that overcome the restrictions that are inherent in the stylised assumption of the representative agent ; • second, how to cope with statistical uncertainty and data revisions, and their impact, in particular, on our estimates of important economic concepts that are relevant for policy formulation but which are not directly observable, like the potential output growth and the equilibrium values of the unemployment rate and the real interest rate ; and, • third, how to deal with possible non - linearities in economic relationships. today ’ s dynamic macroeconomic ( dsge ) models, with their sound microfoundations and ability to ensure internal consistency and invariance of the reduced form parameters to policy changes, have many merits, as i said. but also limitations, and one of them is that they model aggregate economic variables by using one “ representative ” optimising agent whose choices coincide with the aggregate choices of the underlying group of heterogeneous individuals. clearly, this assumption is made for analytical convenience. yet, the conclusions drawn from these models may have far - reaching implications. in real economies, agents are organised into groups and firms, each pursuing their own economic interest. these distinct individual activities are more or less coordinated and some kind of order emerges – this is what adam smith called the “ invisible hand ”. however, the pursuit of individual optimising behaviour does not necessarily imply, a priori, optimal outcomes at an aggregate level. indeed, many interactions in the economy that require coordination among economic agents may result in suboptimal aggregate outcomes, even if all agents pursue the same interests. in addition to coordination failures, models involving representative agents appear particularly ill - suited to address distributional issues, for example those involving employed and unemployed workers. developing richer multiple - agent models is unquestionably difficult but should nevertheless be part of longer - term research agenda, as the potential benefits for policy - making are significant. we live – and have to make decisions – in a world of pervasive uncertainty. however, while the academic profession has made tremendous progress in analysing risk in well - defined stochastic economies, the “ knightian ” uncertainty that confronts central bankers is of an altogether different dimension. among the various forms of uncertainty that central bankers