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2.5 Why combine within and across component methods?

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The rationale behind combining forecasts first within and then across component methods is to equalize the impact of each component method, regardless of whether a component included many forecasts or only a few. For example, while there is only one prediction market that predicts the national popular vote, there are forecasts from numerous econometric models. In such a situation, a simple average of all available forecasts would over-represent models and under-represent prediction markets, which we expect would harm the accuracy of the combined forecast. Another advantage of this approach is that it allows for comparing the accuracy of the different component methods.

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