last decade or more. a third problem relates to data. historical data are subject to measurement error and are also revised after publication. in a number of instances we don ’ t have a consistent series going back far enough to ensure a more reliable estimate of the parameters. in addition, some variables, such as the wealth or expectations variables, have to be proxied. inflation expectations are central to the inflation formation process, yet modelling inflation expectations is a major challenge. while misspecified models, bad methods and inaccurate data are often blamed for serious forecast errors, david hendry argues that they are not the main cause of systematic mistakes. rather it is the unanticipated large changes or shocks within the forecast period that are the primary source of errors. these are totally unpredictable or idiosyncratic events that we simply cannot predict, and in these instances the fault then lies in not rapidly adjusting the forecasts once they become inconsistent with the exogenous shock. when shocks occur, the best we can do is to adjust our forecasts accordingly. this is why we should be constantly monitoring new developments. there are instances where the risks to the forecast may be known but are unquantifiable. the current round of financial market volatility is a case in point. although it was not a totally unpredictable shock, the timing was always uncertain and we still don ’ t know what the ultimate impact will be. lawrence meyer, a former federal reserve governor, in commenting on the federal reserve ’ s reactions to the financial market turbulence, said that the recent shift in policy stance “ tells us how difficult it is to translate financial turbulence into macroeconomic forecast. ” a model - based forecast is only as good as the key assumptions that it is based on. in the forecasting process of the bank, a lot of time and effort is dedicated to the process of deciding on the exogenous assumptions of the model. staff at various levels make inputs, but ultimately, about two to three weeks in advance of the mpc meeting, the mpc members meet to finalise the assumptions. making assumptions about exogenous variables is an important component of the forecasting process. but it is not always easy. take for example the international oil price for example. it is one of the most important exogenous assumptions in our model and we have to formulate a view of the price over the coming three years. unpredictable geopolitical events, hurricanes etc., will surprise us over the

t t mboweni : artistic science or scientific art? – the role of forecasting in monetary policy formulation address by mr t t mboweni, governor of the south african reserve bank, at the reuters economist of the year award, johannesburg, 6 september 2007. * * * honoured guests, ladies and gentlemen 1. introduction it is said that there are two kinds of economists : those who cannot forecast, and those who don ’ t know that they cannot forecast. we have to be thick - skinned to be economists as we are often the butt of jokes. apparently there are more jokes about economists than any other profession, except perhaps lawyers. it would appear that the negative perceptions that are held about economists can be blamed to a large extent on economic forecasters who, we are told, have accurately forecast eight of the last three recessions. but forecasting is not always a joke, and the quality of the contestants of the reuters economist of the year competition is testament to that. forecasting is a serious and integral part of economic life. any decision, whether an investment decision or a policy decision, or in fact any decision in life that involves taking a view on the future, has to be made on the basis of some forecast. so if we regard forecasting as a joke, then the joke is on us. unfortunately we do not have perfect foresight and therefore we will never be able to forecast perfectly. the best we can do is to strive to create forecasting models that are close approximations of reality which in turn provides a coherent and disciplined framework for making decisions. in my comments to you this morning, i will discuss the role of forecasting and how we use forecasts in the monetary policy decision - making process. 2. models and forecasts there are different ways we can go about generating forecasts. although there may be some forecasters who engage in pure guesswork or thumb - sucking, most forecasters would be informed by models with varying degrees of sophistication. these could vary from simple extrapolation of the past, to analysing current developments and assessing their implications for the future, to a more complex dynamic stochastic general equilibrium model, which is the latest fad among model builders. forecasting success, however, is not guaranteed by the level of sophistication of the model. the type of model we use would, to some extent, depend on the time horizon that we are interested in, as different models are better suited to