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。在国际关系中的理论工作的基础上，共同评估者为短期和中期冲突预测开发了多元，贝叶斯矢量自动进程时间序列模型（BVARS），以及分析各种类型的反事实，更具体地说，更具体地说，是这种类型的Markov-switching模型（MS-BVARS），用于Balkans，Israian israeli-part and Israisian-pand-pand-pan and-passinian-pand-pan and-palisissianian-pand-passinian-palisiss-palisiss和印度。这些模型将纳入和测试有关冲突阶段的理论工作，以及对选举和民主过渡对冲突的影响的研究结果。将使用来自两个来源（KEDS/TABARI和IDEA）的缩放事件数据来估计模型。模型将产生短期和中期，案例特定的，针对冲突与合作的定量预测；他们最终将纳入对冲突的福利后果的评估。最后，通过开发模型系数的先验，并为模型的预测构建后验推断，在政治科学中的第一次中之一，将产生模型不确定性对（政治）预测准确性的影响的明确评估。提议的理论和实用价值。首先，它将产生冲突阶段序列的统计上声音特征。研究人员正式测试了巴尔干，以色列 - 帕勒斯坦和印第安 - 巴基斯坦冲突的相位数量，并提供了这些阶段之间过渡概率的数值估计（以及这些估计精确度的量度）。然后，他们将根据特定冲突阶段的实现以及每种情况的冲突阶段的稳态概率，对三个冲突的未来过程进行定量，每周和每月的预测。此外，拟合的MS-BVAR将阐明三个冲突动态的相似性和差异。例如，拟合的模型将表明是否存在持久程度的持久程度，这三个冲突表现出相同的互惠和三角形模式的程度，是否趋向于相同的长期（固定）平均冲突水平趋向于相同的（固定）平均水平，以及为民主的选举力量和转移提供（来自）民主的预测能力是否增强了MS-Bvars的预测能力。冲动反应分析将对假设，惊喜和平计划的可能影响（在冲突阶段）产生影响。条件预测的方法（与BVAR和MS-BVARS）将用于分析冲突的反事实历史。例如，通过在1990年代后期插入巴基斯坦选举的反事实变量，我们将研究该国的反事实后果不会在与印度的冲突中遭受民主逆转。结果将通过多种方式传播。首先，将构建一个网站。该网站将包含调查人员的计算机代码，数据和示例。它还将包含有关如何在选定的国际冲突中构建和应用BVAR和MS-BVAR的教程。其次，调查人员将提供有关如何构建和应用MS-BVARS的缩写。这些课程将在国际研究协会（ISA），和平科学学会（PSS）和美国政治科学协会（APSA）会议等聚会上提供。每一项努力都将在这些短期课程和培训课程中包括“非代表性群体”。从科学上讲，该项目将证明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。美国和全球社会将从能够更好地预期未来几周和几个月的国际冲突中受益，并能够评估各种反事实。