Flexible Bayesian Econometric Modeling

灵活的贝叶斯计量经济学建模

• 批准号: 9819444
• 负责人: John Geweke
• 金额: --
• 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: 1999
• 资助国家: 美国
• 起止时间: 1999-07-01 至 1999-07-09
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9819444&HistoricalAwards=false
• 关键词: Flexible; Bayesian; Econometric; Modeling

The objective of the proposed research is to advance econometric modeling in two directions. In the first direction, tools are developed that (a) significantly expand the universe of data sets that can be used for testing and estimation models of economic behavior and (b) drastically reduce the time and effort required to conduct formal statistical inference in newly formulated models. Tn. the second direction, econometric models are formulated that (a) impose assumptions that are closer to those of economic theory and (b) permit greater flexibility in those aspects of models to which economic theory does not speak, than is now the case.Two aspects of the methods to be employed are common to these objectives. The first aspect is that inference in econometric models is Bayesian, conditioning on the data and maintaining consistency with the likelihood principle. The second aspect is that simulation methods are used to solve key technical problems that cannot be attacked analytically. In the first direction of advance, forward simulation from prior and data densities will be used to permit inference about models, parameters, and functions of interest conditional on selected features of the data. The ability to use selected features of the data enables expansion in the universe of data sets that can be used. As opposed to backward simulation methods like Markov chain Monte Carlo or simulated method of moments, these forward simulators are very easy to construct. This simplicity greatly reduces the development time in econometric modeling. In the second direction of advance, very flexible distributional assumptions will be used to permit the data, as opposed to arbitrary but technically convenient assumptions, to drive inference. These assumptions include multivariate normal mixture models with conditional dependence on covariates or state variables. Restrictions from economic theory, such as concavity, can beimposed in these models by means of appropriate prior distributions and Markov chainMonte Carlo posterior simulation methods.The objectives of the project significantly enhance the capabilities for empirical work in economics, and related disciplines like sociology and political science. The outcome of the project will include state-of-the-art, user friendly software that will be made freely available. This software, in turn, will become part of a web-based educational and software system being developed in this and related projects. Graduate student participation in the project is essential to these outcomes, and will be complementary tothese students' dissertation research.

拟议的研究的目标是推进计量经济学建模在两个方向。在第一个方向上，开发的工具（a）显着扩大了可用于测试和估计经济行为模型的数据集的范围，（B）大大减少了在新制定的模型中进行正式统计推断所需的时间和精力。TN.第二个方向，计量经济学模型的制定，（a）强加的假设更接近于经济理论的假设，（B）允许更大的灵活性，在那些方面的模型，经济理论不说话，比现在的情况。第一个方面是，计量经济学模型中的推理是贝叶斯的，以数据为条件，并与似然原理保持一致。第二个方面是用仿真的方法来解决无法解析攻击的关键技术问题。在第一个方向的进步，从以前的和数据密度的前向模拟将被用来允许有关的模型，参数和功能的推理条件的数据的选定功能。使用数据的选定特征的能力使得能够扩展可使用的数据集的范围。与马尔可夫链蒙特卡罗或模拟矩量法等反向模拟方法相反，这些正向模拟器非常容易构建。这种简单性大大减少了计量经济学建模的开发时间。在第二个前进方向，将使用非常灵活的分布假设来允许数据，而不是任意但技术上方便的假设，以推动推理。这些假设包括对协变量或状态变量具有条件依赖性的多元正态混合模型。通过适当的先验分布和马尔可夫链蒙特卡罗后验模拟方法，可以将经济理论的限制（如随机性）强加于这些模型中，该项目的目标显著提高了经济学以及社会学和政治学等相关学科的实证工作能力。该项目的成果将包括最先进的、用户友好的软件，这些软件将免费提供。反过来，该软件将成为本项目和相关项目中正在开发的基于网络的教育和软件系统的一部分。研究生参与该项目对这些成果至关重要，并将补充这些学生的论文研究。