:orphan:
:title: Ignoramus, Ignorabimus? On Uncertainty in Ecological Inference
.. bibsource:: ../publications.bib
.. pub-details:: elff.gschwend.johnston:ignoramus
.. abstract::
Models of ecological inference (EI) have to rely on crucial assumptions
about the individual-level data-generating process, which cannot be
tested because of the unavailability of these data. However, these
assumptions may be violated by the unknown data and this may lead to
serious bias of estimates and predictions. The amount of bias, however,
cannot be assessed without information that is unavailable in typical
applications of EI. We therefore construct a model that at least
approximately accounts for the additional, nonsampling error that may
result from possible bias incurred by an EI procedure, a model that
builds on the Principle of Maximum Entropy. By means of a systematic
simulation experiment, we examine the performance of prediction intervals
based on this second-stage Maximum Entropy model. The results of this
simulation study suggest that these prediction intervals are at least
approximately correct if all possible configurations of the unknown data
are taken into account. Finally, we apply our method to a real-world
example, where we actually know the true values and are able to assess
the performance of our method: the prediction of district-level
percentages of split-ticket voting in the 1996 General Election of New
Zealand. It turns out that in 95.5% of the New Zealand voting districts,
the actual percentage of split-ticket votes lies inside the 95%
prediction intervals constructed by our method.