Increasingly, political science researchers are turning to Markov chain Monte Carlo methods to solve inferentialproblems with complex models and problematic data. This is an enormously powerful set of tools based on replacingdifficult or impossible analytical work with simulated empirical draws from the distributions of interest.While practitioners are generally aware of the importance of convergence of the Markov chain, many are not fully awareof the difficulties in fully assessing convergence across multiple dimensions. In most applied circumstances\emphevery parameter dimension must be converged for the others to converge. The usual culprit is slow mixing ofthe Markov chain and therefore slow convergence towards the target distribution. This work demonstrates the partialconvergence problem for the two dominant algorithms and illustrates these issues with empirical examples.
It is commonly believed by pundits and political elites that higher turnout favors Democratic candidates,but the extant research is inconsistent in finding this effect. The purpose of this article is to provide scholarswith a methodology for assessing the likely effects of turnout on an election outcome using simulations based onsurvey data. By varying simulated turnout rates for five U.S. elections from 1960 to 2000, we observe thatDemocratic advantages from higher turnout (and Republican advantages from lower turnout) have steadily ebbed since1960, corresponding to the erosion of class cleavages in U.S. elections.