this year, it is difficult to assess its effectiveness comprehensively. however, it could be instructive to review ex ante the robustness of the analytical toolbox assigned to the esrb, and consider ex post whether such a toolbox would have enhanced policymakers understanding of systemic risk prior to the crisis. before doing so, i should make clear that the purpose of the esrb is not to predict crises. the academic literature on early warning signal models, developed over the years, suggests that, for a number of reasons, the scope for error in predicting crises is substantial. either the eruption of a crisis is missed or a period is wrongly identified by the start of a crisis. for this reason, the esrb ’ s analytical toolbox is based on early warning models and indicators designed to identify emerging vulnerabilities or imbalances that could lead to financial instability. these models aim to perform three functions in particular. first, to identify the variables which are associated with financial instability. to this end, an index of aggregate macro - financial imbalances, or of institution - specific vulnerabilities, is tested against variables such as credit growth, property price changes, private sector leverage and current account deficits, to discover which variables predict that index. measures of bank leverage, balance sheet growth and maturity mismatches may also be used in order to gauge the extent to which the financial sector is driving developments. second, to assess the relative importance of different risks for financial stability. this is done using “ adverse scenarios ” that are designed and macro stress - tested to establish the potential severity of different types of risk and the overall resilience of the financial system to severe shocks. such models, including top - down stress test of individual institutions, allow risks to be assessed more broadly and at a higher frequency than is possible with bottom - up exercises run by financial institutions, thus enhancing the “ real time ” information on systemic risk available to policymakers. third, to assess the financial channels through which risk can be propagated, thereby factoring - in potential second - round effects. the use of contagion and spillover models allows, for example, the assessment of the impact on the financial system of the failure of a particular financial institution or of turbulence in a particular sovereign debt market. bis central bankers ’ speeches we could consider how this framework would have functioned if applied retrospectively. for example, the monitoring of the credit - to - gdp ratio as an early warning

labor income shares ”, imf world economic outlook, april. 8 see for example brynjolfson, e., rock, d., syverson, c., ( 2018 ), ‘ artificial intelligence and the modern productivity paradox : a clash of expectations and statistics ’, chapter 1 in the economics of artificial intelligence : an agenda, nber, gordon, r. j. ( 2018 ), ‘ declining american economic growth despite ongoing innovation ’, explorations in economic history, vol. 69, pp. 1 - 12, mokyr, j. ( 2018 ), ‘ the past and the future of innovation : some lessons from economic history ’, explorations in economic history, vol. 69, pp. 13 – 26. 9 the direction of the bias for the aggregate index is less certain. in broda, c. and weinstein, d. ( 2010 ), “ product creation and destruction : evidence and price implications ”, american economic review, vol. 100 ( 3 ), pp. 691 – 723, the authors find that failing to take into account product creation across the index creates an upward bias to traditional “ fixed good ” indices. 10 gorodnichenko, y. and talavera, o. ( 2017 ), “ price setting in online markets : basic facts, international comparisons and cross - border integration ”, american economic review, vol. 107 ( 1 ), pp. 249 – 282, january. 11 cavallo, a. ( 2018 ), “ more amazon effects : online competition and pricing behaviour ”, nber working paper no 25138, presented at the jackson hole symposium 2018. 12 nevertheless, early evidence suggests that online prices are as unresponsive to macroeconomic shocks as offline prices – see gorodnichenko, y., sheremirov, v. and talavera, o. ( 2018 ), “ the responses of internet retail prices to aggregate shocks : a high - frequency approach, ” economics letters, vol. 164 ( c ), pp. 124 – 127. 13 in cavallo, a. ( 2017 ), “ are online and offline prices similar? evidence from large multi - channel retailers ”, american economic review, vol. 107 ( 1 ), pp. 283 – 303, the author finds price levels are identical around 72 % of the time. 14 gorodnichenko, y. ( 2018 ), discussion of cavallo, a.