Exploring the Dynamics of Latent Variable Models

Abstract

While latent variable models are increasingly common in political science research, there is little consensus on how these models are best adapted to accommodate time-series, cross-sectional data. In this paper, we investigate new approaches for modeling dynamic political processes. We begin by examining existing dynamic modeling strategies and outline the properties that make these models poorly suited to modeling time series data, particularly when the latent variables are subject to periods of volatility. We then develop two new models: a robust dynamic latent variable model and a finite mixture model that nests both the static and dynamic approaches. Simulation results indicate that the robust dynamic model outperforms all other modeling strategies. We then conduct replications of existing studies that model judicial preferences and democracy as latent traits. We find that the robust model performs as as well if not better than traditional models. In addition, for the case of judicial ideology, the robust model leads to new evidence about the strategic nature of judicial action.