. there is no unique model or best approach to the forecasting processes and procedures in central banks. this is one reason why the south african reserve bank has adopted the socalled suite of models approach. central banks try to pursue full - information strategies. so, instead of relying on just one model to produce the monetary policy committee ( mpc ) forecast, we employ a large variety of different models. also, since the structures of the various models are diverse, each one is capable of telling a different story. the suite - ofmodels approach has the added advantage of taking some degree of model uncertainty into account by taking all relevant and potentially - important information into consideration. there are essentially two different types of models. the first is the structural macroeconomic type of model which includes the traditional multi - equation model, and the second is a shortterm time series forecast model. each has its own set of benefits and disadvantages. for example, we have the higher frequency autoregressive integrated moving average ( arima ) models which are useful for short - term forecasts but are incapable of predicting a turning point since they are essentially based on their own historic trends. the structural multiequation models such as the south african reserve bank ’ s core econometric model is more suited for policy analysis and forecasting since it incorporates behavioural relationships between the key variables of the model. other structural models such as the dynamic stochastic general equilibrium models ( dsge ) and quarterly projection models ( qpm ) are more reliable over the medium to longer term but are extremely sensitive to the initial conditions or data starting points of the forecast. this is also a reason why many central banks that have adopted the dsge approach spend a lot of time and resources on determining exactly where the economy finds itself currently and how far it is from the steady state. because of the lags in the response of inflation to changes in the monetary policy stance, any forward - looking monetary policy framework requires a forecast of inflation. however, forecasts should be used with the required degree of caution. forecasts are, by definition, surrounded by uncertainty, and knowledge about the degree of forecast uncertainty is essential for decision - makers. basically, we can identify five sources of forecast uncertainty : uncertainty about future shocks to endogenous variables ( e. g shocks to consumption and productivity ) ; uncertainty about assumptions concerning exogenous variables ( e. g assumptions about oil prices ) ; uncertainty about model parameters ( e.

g marginal propensity to consume ) ; uncertainty about data ( e. g revisions of gdp ) ; and uncertainty about the model structure and specification. because of these uncertainties, it becomes extremely difficult to assess the balance of the various influences on the central forecast. mervyn king, governor of the bank of england, has therefore argued that we should not focus too much on the central forecast : “ the monetary policy committee has always stressed that a projection is a probability distribution for possible outcomes and not a single path for either growth or inflation ”. ( mervn king, bank of england, 2009 ) since their impact depends on the variable in question and the forecast horizon under scrutiny, to actually prioritise or rank the source of forecast uncertainty is a difficult task,. however, in any given model, the degree of uncertainty related to future shocks and the assumed trend in the exogenous variables generally pose the greatest threat to aggregated forecast uncertainty, rather than the uncertainty related to the estimated parameters and data. but challenges also relate to the structural specification of the models used by central banks as it is difficult to incorporate everything that really matters in a simplified econometric framework of the real world. for example, the traditional models were not able to capture the current crisis. david blanchflower of the bank of england recently commented on the use of misspecified models or models that fail to take crucial financial issues into consideration : “ the economic models that provided the rationale for the inflation targeting approach do not describe well the features of the global economy that led to the current crisis ; the build up of global imbalances, perceptions of risk in the economy and stresses within the financial sector. ” ( david blanchflower, bank of england, 2009 ) as these factors are extremely difficult to incorporate into our models, building models that capture these issues will be a challenge to model builders. apart from specification problems, there is the issue of exogenous assumptions that get fed in to the model. a forecast generated by even the most perfect model will only be as good as the assumptions that are used. very often these exogenous assumptions are notoriously difficult to predict. for example, in 2007 and 2008 when the international oil prices were increasing, we were continuously surprised on the upside by oil price developments. we were also caught by surprise by the degree to which oil prices declined in the second half of 2008. in the forecasting process our modelling team spends a lot of time analysing the