Applications of modern methods for analyzing data with missing values, based primarily
on multiple imputation, have in the last half-decade become common in American politics
and political behavior. Scholars in these fields have thus increasingly avoided the biases
and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation.
However, researchers in much of comparative politics and international relations,
and others with similar data, have been unable to do the same because the best available
imputation methods work poorly with the time-series cross-section data structures
common in these fields. We attempt to rectify this situation. First, we build a multiple
imputation model that allows smooth time trends, shifts across cross-sectional units, and
correlations over time and space, resulting in far more accurate imputations. Second, we
build nonignorable missingness models by enabling analysts to incorporate knowledge from
area studies experts via priors on individual missing cell values, rather than on difficult-tointerpret
model parameters. Third, because these tasks could not be accomplished within
existing imputation algorithms, in that they cannot handle as many variables as needed
even in the simpler cross-sectional data for which they were designed, we also develop a
new algorithm that substantially expands the range of computationally feasible data types
and sizes for which multiple imputation can be used. These developments also made it
possible to implement the methods introduced here in freely available open source software
that is considerably more reliable than existing algorithms.
