  This paper proposed a methodology to forecast electoral outcomes using the
result of the combination of a fundamental model and a model-based aggregation
of polls. We propose a Bayesian hierarchical structure for the fundamental
model that synthesises data at the provincial, regional and national level. We
use a Bayesian strategy to combine the fundamental model with the information
coming for recent polls. This model can naturally be updated every time new
information, for instance a new poll, becomes available. This methodology is
well suited to deal with increasingly frequent situations in which new
political parties enter an electoral competition, although our approach is
general enough to accommodate any other electoral situation. We illustrate the
advantages of our method using the 2015 Spanish Congressional Election in which
two new parties ended up receiving 30\% of the votes. We compare the predictive
performance of our model versus alternative models. In general the predictions
of our model outperform the alternative specifications, including hybrid models
that combine fundamental and polls models. Our predictions are, in relative
terms, particularly accurate in predicting the seats obtained by each political
party.
