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 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