Collaborative Research: Bayesian Times Series Models for the Analysis of International Conflict

合作研究：用于分析国际冲突的贝叶斯时间序列模型

• 批准号: 0351179
• 负责人: John Freeman
• 金额: $ 19.55万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0351179&HistoricalAwards=false
• 关键词: Collaborative; Research; Bayesian; Times; Series

International war and conflict threaten the lives and well-being of millions of people. On the basis of theoretical work in international relations, the co-investigators develop multivariate, Bayesian vector autoregression time series models (BVARs) for short and medium term conflict forecasting and for the analysis of counterfactuals of various kinds, more specifically, Markov-switching models of this type (MS-BVARs) for the Balkans, Israeli-Palestinian, and India-Pakistan conflicts. The models will incorporate and test theoretical work on conflict phase shifts as well as the results of research on the impact of elections and democratic transitions on conflict. Scaled events data from two sources (KEDS/TABARI and IDEA) will be used to estimate the model.The models will yield short and medium term, case specific, quantitative predictions of conflict and cooperation; they eventually will incorporate assessments of the welfare consequences of conflict. Finally, by developing priors for the model coefficients and constructing posterior inferences for the models' predictions, for one of the first times in political science, explicit assessments of the impact of model uncertainty on (political) forecasts accuracy will be produced.Intellectual Merit. The proposed has theoretical and practical value. To begin, it will produce statistically sound characterizations of conflict phase sequences. The investigators test formally for the number of phases in the Balkans, Israeli-Palestinian, and Indian-Pakistani conflicts, and also provide numerical estimates of the transition probabilities between these phases (along with measures of the precision of these estimates). They then will produce quantitative, weekly and monthly predictions of the future course of the three conflicts conditional on the realization of specific conflict phases and on the steady state probabilities of the conflict phases for each case. In addition, the fitted MS-BVARs will illuminate similarities and differences in the three conflicts' dynamics. For instance, the fitted models will show if there are common degrees of persistence in them, the extent to which the three conflicts display the same patterns of reciprocity and triangularity, whether the conflicts tend toward the same long-term (fixed) mean levels of conflict, and whether provision for electoral forces and transitions to (from) democracy enhance the predictive power of the MS-BVARs. Impulse response analysis will yield insights into the possible impact of hypothetical, surprise peace initiatives by third parties (conditional on the conflict phase). The methods of conditional forecasting (with the BVARs and MS-BVARs) will be used to analyze counterfactual histories of the conflicts. For example, by inserting counterfactual variables for elections in Pakistan in the late 1990s we will examine of the counterfactual consequences of that country not experiencing a democratic reversal on its conflict with India.Broader impact. The results will be disseminated in several ways. First, a web-site will be constructed. The website will contain the investigators' computer code, data, and examples. It also will contain a tutorial on how to construct and apply BVARs and MS-BVARs for selected international conflicts. Second, the investigators will offer short-courses on how to build and apply MS-BVARs. These courses will be offered at such gatherings as the International Studies Association (ISA) , Peace Science Society (PSS), and American Political Science Association (APSA) meetings. Every effort will be to include "unrepresentative groups" in these short course and training sessions. Scientifically, the project will demonstrate the usefulness of events data in political forecasting, and advance our understanding of Bayesian time series methods in the social sciences. American and global society will benefit from being better able to anticipate international conflict weeks and months ahead and also being able to evaluate counterfactuals of various kinds.

国际战争和冲突威胁着数百万人的生命和福祉。在国际关系理论工作的基础上，共同研究者开发了多变量贝叶斯向量自回归时间序列模型（BVAR），用于短期和中期冲突预测，并用于分析各种反事实，更具体地说，巴尔干半岛，以色列-巴勒斯坦和印度-巴基斯坦冲突的马尔可夫转换模型（MS-BVAR）。这些模型将纳入并检验关于冲突阶段转变的理论工作以及关于选举和民主过渡对冲突的影响的研究结果。从两个来源（KEDS/TABARI和IDEA）的规模事件数据将被用来估计模型。模型将产生短期和中期，具体情况下，冲突与合作的定量预测，他们最终将纳入冲突的福利后果的评估。最后，通过建立模型系数的先验知识和构建模型预测的后验推理，将对模型不确定性对（政治）预测准确性的影响进行明确的评估，这在政治学中尚属首次。本文的研究具有一定的理论意义和实用价值。开始，它将产生冲突相序列的统计上合理的表征。调查人员正式测试了巴尔干半岛、巴以冲突和印巴冲突的阶段数，并提供了这些阶段之间过渡概率的数值估计（沿着这些估计的精确度）。然后，他们将根据具体冲突阶段的实现情况和每种情况下冲突阶段的稳定状态概率，对这三种冲突的未来进程作出定量、每周和每月的预测。此外，拟合的MS-BVAR将阐明三种冲突动态的相似性和差异。例如，拟合模型将显示它们是否存在共同的持续性程度，三种冲突显示相同的互惠和三角模式的程度，冲突是否倾向于相同的长期（固定）平均冲突水平，以及选举力量和向（从）民主过渡的规定是否增强了MS-BVAR的预测能力。脉冲反应分析将使人们深入了解第三方（以冲突阶段为条件）假设的、突然的和平倡议可能产生的影响。条件预测方法（使用BVAR和MS-BVAR）将用于分析冲突的反事实历史。例如，通过插入20世纪90年代末巴基斯坦选举的反事实变量，我们将考察该国在与印度的冲突中没有经历民主逆转的反事实后果。调查结果将以多种方式传播。首先，将建立一个网站。该网站将包含研究人员的计算机代码、数据和示例。它还将包含一个关于如何为选定的国际冲突制定和应用最大价值评估和MS-BVAR的教程。其次，研究人员将提供关于如何建立和应用MS-BVAR的短期课程。这些课程将在国际研究协会（伊萨），和平科学学会（PSS）和美国政治学协会（APSA）会议等聚会上提供。将尽一切努力将“无代表性群体”纳入这些短期课程和培训班。科学上，该项目将展示事件数据在政治预测中的有用性，并促进我们对社会科学中贝叶斯时间序列方法的理解。美国和全球社会将受益于能够更好地预测未来几周和几个月的国际冲突，并能够评估各种反事实。