Hostage taking: Understanding terrorism event dynamics

School of Economic, Political and Policy Sciences, GR 31, 800 W. Campbell Road, The University of Texas at Dallas, Richardson, TX 75080-3021, United States
Journal of Policy Modeling (Impact Factor: 0.64). 01/2009; 31(5):758-778. DOI: 10.1016/j.jpolmod.2008.07.003
Source: RePEc

ABSTRACT This paper employs advanced time series methods to identify the dynamic properties of three hostage taking series. The immediate and long run multipliers of three covariates--successful past negotiations, violent ends, and deaths--are identified. Each hostage series responds differently to the covariates. Past concessions have the strongest impact on generating future kidnapping events, supporting the conventional wisdom to abide by a stated no-concession policy. Each hostage series has different changepoints caused by a variety of circumstances. Skyjackings and kidnappings are negatively correlated, while skyjackings and other hostage events are positively correlated. Policy recommendations are offered.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Multivariate count models are rare in political science despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects of exogenous covariate shocks are illustrated for the model. Two full illustrations of the model, its interpretations, and results are presented. The first example is a dynamic model that reanalyzes the patterns and predictors of superpower rivalry events. The second example applies the model to analyze the dynamics of transnational terrorist targeting decisions between 1968 and 2008. The latter example's results have direct implications for contemporary policy about terrorists' targeting that are both novel and innovative in the study of terrorism.
    Political Analysis 07/2012; 20(3):292-315. · 2.19 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Researchers working with panel data sets often face situations where changes in unobserved factors have produced changes in the cross-sectional heterogene- ity across time periods. Unfortunately, conventional statistical methods for panel data are based on the assumption that the unobserved cross-sectional heterogene- ity is time constant. In this paper, I introduce statistical methods to diagnose and model changes in the unobserved heterogeneity. First, I develop three com- binations of a hidden Markov model with panel data models using the Bayesian framework; (1) a baseline hidden Markov panel model with varying xed eects
  • [Show abstract] [Hide abstract]
    ABSTRACT: This article investigates the determinants of logistical and negotiation successes in hostage-taking incidents using an expanded dataset that runs from 1978 to 2010. Unlike an earlier study, the current study has a rich set of negotiation variables in addition to political, geographical, and organizational variables associated with the perpetrators or targets of the attacks. The 33 years of data permit a split into two subperiods: 1978–1987 and 1988–2010, before and after the rise of religious fundamentalist terrorist groups. Logistical success depends on resource and target vulnerability proxies, while negotiation success hinges on bargaining variables. Among many novel findings, democracy significantly hampers logistical success throughout the entire period. Kidnappings, tropical climates, and high elevations foster logistical success. Religious fundamentalist terrorists’ logistical advantage during 1978–1987 was lost during 1988–2010. Abducting protected persons, making demands on the host country, and staging incidents in a democracy limit negotiation success for the terrorists. If terrorists moderate or replace one or more demands, the likelihood of negotiation success for the terrorists goes up.
    Public Choice 01/2013; 156(1-2). · 0.91 Impact Factor

Full-text (2 Sources)

Available from
May 27, 2014