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Below results based on the criteria 'Count Data'
Total number of records returned: 2
Unemployment and Violence in Northern Ireland: a missing data model for ecological inference
Contrary to the body of literature in political violence, and the rhetoric of many parties of the conflict, time-series models of ``the troubles'' in Northern Ireland by White (1993) and Thompson (1989) have found no evidence that economic conditions effect the intensity, sources or direction of violence. I show that several methodological flaws exist in previous models. They fail to address the discrete, count nature of the data, the contagion present from aggregation over time, pooling issues from different types of violence, and the over dispersal of deaths. However, the key problem, acknowledged even by the authors themselves, is that all measures of unemployment aggregate Protestant and Catholic unemployment rates into one single measure. Using a model that combines methods of Multiple Imputation to recover missing data (King Honaker Joseph Scheve 2001) and the literature of models for Ecological Inference problems (especially King 1997) I estimate the disaggregated unemployment rates by religion from the available data. Unemployment is shown to be a leading cause of the violence by Republican factions in Northern Ireland.
A Comparison of the Small-Sample Properties of Several Estimators for Spatial-Lag Count Models
Political scientists frequently encounter and analyze spatially interdependent count data. Applications include counts of coups in African countries, of state participation in militarized interstate disputes, and of bills sponsored by members of Congress, to name just a few. The extant empirical models for spatially interdependent counts and their corresponding estimators are, unfortunately, dauntingly complex, computationally costly, or both. They also generally tend 1) to treat spatial dependence as nuisance, 2) to stress spatial-error or spatial-heterogeneity models over spatial-lag models, and 3) to treat all observed spatial association as arising by one undifferentiated source. Prominent examples include the Winsorized count model of Kaiser and Cressie (1997) and Griffithâ��s spatially-filtered Poisson model (2002, 2003). Given the available options, the default approaches in most applied political-science research are to either to ignore spatial interdependence in count variables or to use spatially-lagged observed-counts as exogenous regressors, either of which leads to inconsistent estimates of causal relationships. We develop alternative nonlinear least-squares and method-of-moments estimators for the spatial-lag Poisson model that are consistent. We evaluate by Monte Carlo simulation the small sample performance of these relatively simple estimators against the naiive alternatives of current practice. Our results indicate substantial consistency improvements against minimal complexity and computational costs. We illustrate the model and estimators with an analysis of terrorist incidents around the world.