New Evidence on the Value of Combining Forecasts

Winter 2009 Issue 12 FORESIGHT One of the major findings of forecasting research over the last quarter century has been that greater predictive accuracy can often be achieved by combining forecasts from different methods or sources. Combination can be a process as straightforward as taking a simple average of the different forecasts, in which case the constituent forecasts are all weighted equally. Other, more sophisticated techniques are available too, such as trying to estimate the optimal weights that should be attached to the individual forecasts, so that those that are likely to be the most accurate receive a greater weight in the averaging process. Researchers continue to investigate circumstances where combining may well be useful to forecasters and to compare the accuracy of different approaches to combining forecasts. FORECAST COMBINATION AND THE BANK OF ENGLAND’S SUITE OF STATISTICAL FORECASTING MODELS George Kapetanios and his colleagues (Kapetanios et al., 2008) have recently evaluated the potential advantages of combining forecasting data at the Bank of England, where quarterly forecasts of inflation and GDP growth are made. The bank has a suite of different statistical forecasting methods available. They include extremely simple approaches, such as the naïve (or random walk) method where the forecasts are equal to the most recent observation. More sophisticated and complex methods in the suite include autoregression, vector-autoregressions (VARs), Markov switching models, factor models, and time-varying coefficient models.