Bayesian multilevel analysis of binary time-series cross-sectional data in political economy

In this dissertation project, I propose a Bayesian generalized linear multilevel model with pth order autoregressive errors (GLMM-AR(p)) for modeling inter-temporal dependence, contemporary correlation, and heterogeneity of unbalanced binary Time-Series Cross-Sectional data. The model includes two unnested sources of clustering in the unit- and time-dimensions for analyzing heterogeneities and contemporal correlation which are salient in the era of globalization. Group-level variations are further explained with unit- and time-specific characteristics. For handling dynamics in politics and political economy, I apply the autoregressive error specification to analyze serial correlation which may not be fully captured by the selected covariates.;Two applications on civil war and sovereign default demonstrate how the proposed model controls for multiple potential confounders. It also improves reliability of statistical inferences and helps forecasts by more efficiently using the information in data. The first application focuses on the causal relationship between ethnic minority rule and civil war onset. The GLMM-AR(p) model helps study those background factors which affect the relationship under investigation. The second applied study considers how regime duration affects sovereign default conditional on regime type by putting the national policy-making regarding repaying external debt into the international context. To model the heterogeneous vulnerability or sensitivity of the developing countries to global shocks, I extend the GLMM-AR(p) model to analyze time-specific unit-varying effects.