we could not answer 30 years ago. large dynamic stochastic general equilibrium models – the ones that failed to incorporate financial frictions – require the use of involved numerical methods to produce an estimate of the posterior distribution when confronted with the data. advances in computing power have allowed the estimation of such models, greatly expanding the tools central banks have at hand to analyse the economy. yet, current conventional estimation methods still imply many hours, or even days, of estimation of more complex models that include household and firm heterogeneity. your generation will push out the modelling frontier even farther. no doubt, parallel and cloud computing, machine learning and artificial intelligence are already transforming the economics profession. you will all need to keep up with the pace of technological progress. economics is life - long learning. many of the methods and software packages you learn today will be outdated by the time you are mid - way through your career. at the same time, big data and richer and timelier datasets will help improve the input to our models. the “ billion prices project ”, which was launched by the massachusetts institute of technology ( mit ) in 2008, publishes daily online price indices for more than 20 countries where price information is collected automatically through machines from hundreds of retailers. i would hazard a guess that you will look back with pity on the current generation of economists who have only just started using online and scanner data to improve our short - term forecasts. the sky is the limit. and, fourth, structural changes perhaps contribute most to making the economics profession exciting, but also challenging. if you had been working in the ecb ’ s monetary policy department in 2014, you would have more than likely contributed to the work on the term structure models i have alluded to previously, or on financial frictions and euro area heterogeneity. but if you come to work in the ecb ’ s monetary policy department in 2021, you will probably be working on the consequences of fintech for financial intermediation, or on the impact of trade wars and dislocating global value chains on price and output spillovers in the global economy. your studies today will equip you with the tools to approach such challenges in the future. but you will have to use these tools very flexibly and with an open mind. climate change is perhaps the most far - reaching of the challenges facing your generation. it is no coincidence that william nordhaus was awarded the nobel prize in economic sciences last year for

, for example, thomas ( 1997 ). see fisher ( 2010 ) for a more detailed discussion of these operations. estimating the effects so far so what impact have our asset purchases had to date? that ’ s the two hundred billion pound question. unfortunately, it is hard to provide a definitive answer. to be clear this is no different from our inability to assess precisely the impact of the reduction in bank rate from 5 % to 0. 5 %. or indeed of any macroeconomic policy measure. without knowing what would have happened in the absence of a policy action, it is not possible to identify its incremental impact. but what is different is that this policy instrument is relatively untried and untested. so the demand to provide some insight into its impact is that much greater. this demand is quite understandable, but difficult to satisfy. one possible approach to assessing the impact of our asset purchases is to consider the growth of broad money. a key principle underlying the asset purchase programme is the belief in a causal mechanism running from increased money balances into higher asset prices and nominal spending. this mechanism can be expressed in terms of a process of portfolio rebalancing in which yields of different assets adjust in order for the higher level of money balances to be willingly held. but the same mechanism could be expressed in terms of a monetarist approach, in which asset purchases work through measures of money disequilibrium which in turn spur additional spending. these different frameworks are sometimes presented as conflicting models of the transmission of asset purchases. but i view them as essentially two sides of the same coin. importantly, they share the same fundamental assumption about why monetary policy at the zero bound can stimulate nominal spending : namely, imperfect substitutability between money and other assets. over the past year, despite money - financed asset purchases totalling £200 billion, broad money has increased by just £8 billion. 6 what should we make of this weakness? in part it speaks to the counterfactual. the falls in nominal spending and the desire by banks to reduce leverage means that, absent the monetary injection, broad money would have almost certainly been far weaker. but it is also symptomatic of the scale of the new equity and debt raised by uk banks over this period which, together with retained profits, totals more than £85 billion. given that the non - bank private sector is likely to have been the source for much of this funding, this is