Degree Type

Creative Component

Semester of Graduation

Fall 2019



First Major Professor

Kaiser, Mark


Master of Science (MS)




From 1970 to 2015, there were 7,584 terrorist attacks in Latin America and Asia. We investigate modeling these events using dynamic statistical models with a monthly time step. Methodologically, dynamic models are the most straight forward when based entirely on normal probability structures. A potential complication is that the number of terrorist attacks are counts, with a substantial number of zero values when considered on a monthly basis. We consider a traditional additive error dynamic model in which the mean process evolves through time following an autoregressive structure. The latent process follows normal distributions with these means. The actual observation process is then a discretized version of the latent process. We contrast this model with a model that takes the observation process to follow Poisson distribution directly. The estimation and inference proceed via Markov Chain Monte Carlo methods and the models are assessed based on the combination of the autocorrelations and the maximum number of recorded attacks.

Copyright Owner

Hur, Earl

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