Author's School

Graduate School of Arts & Sciences

Author's Department/Program

Political Science


English (en)

Date of Award

January 2010

Degree Type


Degree Name

Doctor of Philosophy (PhD)

Chair and Committee

Andrew Martin


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, con-temporary 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 corre- lation which are salient in the era of globalization. Group-level variations are further explained with unit- and time-specific characteristics. For handling dynamics in pol- itics 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.


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