Time series of event counts are common in political science and other social science applications. Presently, there are few satisfactory methods for identifying the dynamics in such data and accounting for the dynamic processes in event counts regression. We address this issue by building on earlier work for persistent event counts in the Poisson exponentially weighted moving-average model (PEWMA) of Brandt et al. (American Journal of Political Science44(4):823–843, 2000). We develop an alternative model for stationary mean reverting data, the Poisson autoregressive model of orderp, or PAR(p) model. Issues of identification and model selection are also considered. We then evaluate the properties of this model and present both Monte Carlo evidence and applications to illustrate.
Bayesian approaches to the study of politics are increasingly popular. But Bayesian approaches to modeling multiple time series have not been critically evaluated. This is in spite of the potential value of these models in international relations, political economy, and other fields of our discipline. We review recent developments in Bayesian multi-equation time series modeling in theory testing, forecasting, and policy analysis. Methods for constructing Bayesian measures of uncertainty of impulse responses (Bayesian shape error bands) are explained. A reference prior for these models that has proven useful in short-and medium-term forecasting in macroeconomics is described. Once modified to incorporate our experience analyzing political data and our theories, this prior can enhance our ability to forecast over the short and medium terms complex political dynamics like those exhibited by certain international conflicts. In addition, we explain how contingent Bayesian forecasts can be constructed, contingent Bayesian forecasts that embody policy counterfactuals. The value of these new Bayesian methods is illustrated in a reanalysis of the Israeli-Palestinian conflict of the 1980s.
This article utilizes Bayesian Poisson changepoint regression models to demonstrate how transnational terrorists adjusted their target choices in response to target hardening. In addition, changes in the collective tastes of terrorists and their sponsorship have played a role in target selection over time. For each of four target types— officials, military, business, and private parties—the authors identify the number of regimes and the probable predictors of the events. Regime changes are tied to the rise of modern transnational terrorism, the deployment of technological barriers, the start of state sponsorship, and the dominance of the fundamentalists. The authors also include two sets of covariates—logistical outcome and victim’s nature—to better explain the dynamics. As other targets have been fortified and terrorists have sought greater carnage, private parties have become the preferred target type. In recent years, terrorists have increasingly favored people over property for all target types. Moreover, authorities have been more successful at stopping attacks against officials and the military, thereby motivating terrorists to attack business targets and private parties.
We propose a framework for forecasting and analyzing regional and international conflicts. It generates forecasts that (1) are accurate but account for uncertainty, (2) are produced in (near) real time, (3) capture actors’ simultaneous behaviors, (4) incorporate prior beliefs, and (5) generate policy contingent forecasts. We combine the CAMEO event-coding framework with Markov-switching and Bayesian vector autoregression models to meet these goals. Our example produces a series of forecasts for material conflict between the Israelis and Palestinians for 2010. Our forecast is that the level of material conflict between these belligerents will increase in 2010, compared to 2009.
Previous research has shown that the duration of a civil war is in part a function of how it ends: in government victory, rebel victory, or negotiated settlement. We present a model of how protagonists in a civil war choose to stop fighting. Hypotheses derived from this theory relate the duration of a civil war to its outcome as well as characteristics of the civil war and the civil war nation. Findings from a competing risk model reveal that the effects of predictors on duration vary according to whether the conflict ended in government victory, rebel victory, or negotiated settlement.Civil war, Conflict resolution, Duration, Competing risks,
Do public opinion dynamics play an important role in understanding conflict trajectories between democratic governments and other rival groups? The authors interpret several theories of opinion dynamics as competing clusters of contemporaneous causal links connoting reciprocity, accountability, and credibility. They translate these clusters into four distinct Bayesian structural time series models fit to events data from the Israeli—Palestinian conflict with variables for U.S. intervention and Jewish public opinion about prospects for peace. A credibility model, allowing Jewish public opinion to influence U.S., Palestinian, and Israeli behavior within a given month, fits best. More pacific Israeli opinion leads to more immediate Palestinian hostility toward Israelis. This response's direction suggests a negative feedback mechanism in which low-level conflict is maintained and momentum toward either all-out war or dramatic peace is slowed. In addition, a forecasting model including Jewish public opinion is shown to forecast ex ante better than a model without this variable.
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