stage in the process that are very difficult to evaluate, such as the effects of the lehman brothers bankruptcy in autumn 2008. one central issue with regard to how the monetary policy decision - making process is set up is what stance the decision - makers, that is, the executive board, should take with regard to risks that for various reasons are not directly incorporated in the forecasts. if individual executive board members consider that there are observable risks and suspect that these may have significant consequences, the riksbank will of course try to analyse these in depth. but it is not always easy to quantify such risks, and thus it may be difficult to include them in the actual forecast. the question then is how these risks should be taken into account in the monetary policy decisions. i consider it important to try to quantify as far as possible the risks deemed relevant to the monetary policy decisions. this is particularly so when it comes to risks with a low probability, but which would have substantial consequences for the way the economy develops, such as a burst housing bubble, so it is important to try to quantify how the risks are affected by the various alternative actions. there have recently been a number of discussions regarding whether the low policy rate we have had since last summer increases the risk of a swedish housing market crash further ahead. my personal assessment is that extremely low interest rates lead to some increase in the risk that housing prices will be pushed up to unsustainable levels. but this increased risk must be weighed against the risk of higher interest rates hampering the recovery in the economy. however, without properly quantifying these risks the decision - makers have to rely on their gut feeling in their deliberations. there are many reasons why gut feeling may be a poor guide with regard to making this type of decision. in the field of cognitive research, for instance, one speaks of the neglect of probability bias, that is, a tendency to entirely disregard the probability of various scenarios actually occurring when people make decisions under conditions of uncertainty. it is common to entirely disregard the fact that the scenario may never occur or that one is completely over - estimating the probability of the scenario. this is far from the only cognitive bias that decision - makers might be affected by. but it is one that can be reduced by systematically trying to evaluate the probability of various scenarios occurring. it is also important to try to illustrate how the decision that is to be taken will increase or decrease the probability of these scenarios.

to make forecasts is that monetary policy has an impact with a time lag. the riksbank conducts a policy of flexible inflation targeting. this means that, in addition to stabilising inflation around the target of 2 per cent, the riksbank also aims to stabilise resource utilisation around a normal level. the riksbank varies its steering interest rate, the repo rate, to attain the inflation target. however, the effects of monetary policy on the economy come with a fairly long time lag. this means that prior to each monetary policy decision it is necessary to analyse how different repo rate decisions can be expected to affect inflation and resource utilisation over the coming years. choosing the alternative connected to the forecast that provides the best target fulfilment is sometimes called forecast targeting. 1 a further argument for making forecasts is that the efficiency of monetary policy can be improved through transparency and credibility. by publishing our forecasts and explaining how our monetary policy decisions relate to them we make it easier for the outside world to have correct expectations of how the riksbank will react to new information. it is important that we are clear about how the riksbank views developments and what effects the monetary policy decisions are expected to have. forecasts and uncertainty as i said, forecasts are always uncertain. neither the riksbank nor other forecasters have access to all of the information that has significance for the development of the economy during the forecast period. shocks to the economy can occur suddenly and surprise the forecasters. it is thus impossible to avoid forecasting errors. one example of a time of great uncertainty over future economic developments is the recent financial crisis. one indicator of this uncertainty is reflected in the broad spread between the different analysts ’ forecasts. according to the compilation of around 30 different forecasters ’ assessments in “ consensus forecasts ” ( cf ), which has, for instance, compiled forecasts for gdp growth each month since 1989, the uncertainty over developments in the us economy was the largest it has been in at least 20 years. 2 it was on the whole greater than during the recessions at the beginning of the 1990s and 2000s and, above all, it lasted longer this time. statistics regarding this are only available from 1995 for sweden, but they nevertheless show see the article “ policy expectations and policy evaluations : the role of transparency and communication ” by lars e. o. svensson in sveriges riksbank economic review no