Approximate Bayesian computation in state space models

状态空间模型中的近似贝叶斯计算

• 批准号: DP150101728
• 负责人: Prof Gael Martin
• 金额: $ 19.12万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-04-02 至 2019-07-01
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP150101728
• 关键词: Approximate; Bayesian; computation; state; space

Economic and financial data frequently exhibit dynamic patterns, driven by unobserved processes that relate to the behaviour of economic agents, or to institutional and technological change. To gain insight into such 'latent' processes is of paramount importance in terms of both understanding the economy and producing accurate, readily up-dated, forecasts of its future performance. Using a Bayesian approach, new simulation-based statistical methods for analysing latent variable models are proposed. Emphasis is given to the development of relatively simple techniques that are applicable to a wide range of empirically relevant models, with a view to improving the access of non-specialists to this powerful form of statistical analysis.

经济和金融数据经常显示出动态模式，其驱动力是与经济行为主体的行为或与体制和技术变革有关的未观察到的过程。深入了解这种“潜在”过程对于理解经济和对其未来表现进行准确、及时更新的预测至关重要。使用贝叶斯方法，新的模拟为基础的统计方法，分析潜变量模型的建议。重点是开发适用于各种经验相关模型的相对简单的技术，以改善非专业人员获得这种强大的统计分析形式的机会。