##ration in money and credit growth, and a precipitous fall in output. despite our humble performance as economists in forecasting this specific sequence of events, no one can credibly claim that what actually materialised was the most likely among a range of possible scenarios. i think it is fair to say that in order to contain risks to price stability – whether to the up or the downside – there was a pressing need to head off these excesses, instead of dealing with them after the fact. irrespective of the conjunctural assessment, of how these excesses might eventually unwind and of whether or not this process might affect the financial system, such a response was clearly needed. being agnostic about monetary developments would not have been appropriate. to conclude : the improvement in our tools has been evolutionary, rather than revolutionary. a high degree of continuity has been maintained, as our results support the monetary policy strategy. progress has been achieved through the pursuit of our enhancement agenda, but open questions remain. because of the current financial stability issues, we have been applauded for the monetary analysis, but we are again called to limit its scope. previously we were asked to plug monetary information into an inflation forecast. now we are being asked to plug it into the emerging framework for macro - prudential analysis. as a by - product, monetary analysis may both help and be helped by the analysis of bank balance sheets for macro - prudential purposes. however, we do not want to limit monetary analysis to this end. we need to maintain a comprehensive view of the transmission of money to the economy and to focus on pursuing price stability, in line with our mandate.

different forecast horizons. in the short run, momentum of data may be more important than longer - term structural and behavioural relationships. we therefore see different types of forecasting strategies in the markets. for example, many traders have time horizons of a few minutes. to them tomorrow is very long term. those needing short - term forecasts will probably use chartist or bottom - up spreadsheet techniques. these models have little basis in economic theory, and are unlikely to perform well over longer - term horizons. our structural models in the bank, for example, use quarterly data, so by definition they cannot be used for predicting one month ahead. for this we would use autoregressive integrated moving average ( arima ) models, which are also momentum - type models with no underlying economic theory. predictions based on arima are used for short - term predictions, and since they are based purely on historical trends, they are not very good when it comes to predicting turning points. some forecasters rely on simple correlations noted in the market. as we all know, correlations do not imply a causal relationship or even any direct relationship. the dangers of spurious correlations are well - known. david hendry, the renowned oxford university econometrician, in his appropriately - titled paper : “ forecasting : alchemy or science? ” illustrated this perfectly when he showed that there was a better relationship between inflation and the cumulative rainfall in scotland, than between inflation and monetary aggregates. i don ’ t know if this means that we should be employing scottish weather forecasters in the bank to make our inflation forecasts. i am told, however, that weather forecasters were created in order to make economic forecasters look good. 3. why are forecasts wrong? even if we do build sophisticated structural models incorporating good behavioural relationships, the forecasts are still likely to be wrong. there are various types of forecast errors. firstly, there may be misspecification of the model. this could mean that we have excluded one or more variables, or that we have specified the wrong type of function. our modelling team is continually developing our models to try and overcome this type of problem. secondly, there may be structural breaks in the economy which are difficult to take account of when estimating over a long time horizon. this means that there may be a bias to the estimated coefficients of the model. this is particularly relevant to south africa given the transformation of the economy over the