Abstract
Abstract On Election Night, returns from polling stations occur in a highly non-random manner, thus posing special difficulties in forecasting the final result. Using a data base which contains the results of past elections for all polling stations, a robust hierarchical multivariate regression model is set up which uses the available returns as a training sample and the outcome of the campaign surveys as a prior. This model produces accurate predictions of the final results, even with only a fraction of the returns, and it is extremely robust against data transmission errors.
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