What to Do about Missing Values in Time-Series Cross-Section Data

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 tins subset of political science have thus increasingly avoided the biases mid inefficiencies caused by ad hoc methods like list wise 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 tins situation with three related developments 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 enable analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret 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 woe designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for winch multiple imputation can be used These developments also make it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing algorithms