![]() In a series of experiments with highly qualified MBA students, a majority of participants thought that the average of estimates would reflect only average performance ( Larrick & Soll 2006). What’s not to like about combining? Interestingly, people often lack the intuition to understand the benefits of combining. Under such favorable conditions, combining reduces forecasts errors almost by half ( Graefe et al., 2010). So the key is to use methods and data that differ substantially so as to bracket the true value. The answer is the error of the second forecast would need to be bigger than the error of the first forecast if the error was in the same direction (sign) but more than three times the error of the first forecast if the error had the opposite sign. Imagine, for instance, that you have two forecasts of a quantity: How big would the error of the second forecast need to be in order for the error of the average of the two forecasts to be larger than the error of the first forecast alone? Combining forecasts is especially useful if the forecaster wants to avoid large errors, and if there is uncertainty about which method will be most accurate. In addition, the combined forecasts were often more accurate than the most accurate component. The reductions of forecast error ranged from 3 to 24 percent. ![]() A meta-analysis of 30 studies estimated that the combined forecasts yielded a 12 percent reduction in error compared to the average error of the components. This yields the PollyVote, which stands for “many” and “political.”Ĭombining forecasts is one the most powerful tools available to forecasters. They are polls, the Iowa Electronic Market, econometric models and experts’ judgments. Polly’s formula integrates four different methods. I say “almost” because beyond simply reproducing them, she performs a simple calculation: She averages them. She picks up others’ predictions about election outcomes and repeats them almost exactly as she hears them. Meet “Polly,” the political forecasting expert.
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