Sampling-Based Approaches to Bayesian Inference in Econometrics

计量经济学中基于抽样的贝叶斯推理方法

• 批准号: 9210070
• 负责人: John Geweke
• 金额: $ 18.96万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-08-01 至 1996-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9210070&HistoricalAwards=false
• 关键词: Sampling; Based; Approaches; Bayesian; Inference

The focus of this research is on solutions of the Bayesian multiple integration problem in econometrics, using a class of numerical integration procedures widely known as Gibbs samplers. The objectives of the project are to make Bayesian inference as routine and reliable as classical methods in econometrics, and to provide solutions of outstanding econometric problems on which little progress has been made heretofore using either Bayesian or classical approaches. The project involves extensions of the analysis of Gibbs samplers to develop central limit theorems appropriate to the systematic assessment of numerical accuracy, evaluate senstivity to initial conditions, and apply these methods ot underidentified models. The project will also exploit the successive conditioning fundamental to Gibbs sampling and the related technique of data augmentation, to develop a modular treatment of most econometric models. These modules include semi-informative priors in regressions, censored regression, heteroskedasticity, seemingly unrelated regressions, the multinominal probit model, reduced rank regression, the latent factor model, multivariatre (moving average) autoregression, and state space models.

本研究的重点是解决计量经济学中的贝叶斯多重积分问题，使用一类被广泛称为吉布斯采样的数值积分程序。该项目的目标是使贝叶斯推理像计量经济学中的经典方法一样常规和可靠，并为迄今为止使用贝叶斯方法或经典方法都没有取得进展的突出计量经济学问题提供解决方案。该项目涉及吉布斯采样分析的扩展，以发展适合于系统评估数值精度的中心极限定理，评估对初始条件的敏感性，并将这些方法应用于未充分识别的模型。该项目还将利用Gibbs抽样的连续条件反射基础和相关的数据增强技术，对大多数计量经济模型进行模块化处理。这些模块包括回归中的半信息先验、删节回归、异方差性、看似不相关的回归、多项概率模型、降秩回归、潜在因素模型、多变量（移动平均）自回归和状态空间模型。