Bayesian Inference, Modeling and Forecasting Techniques in Econometrics

计量经济学中的贝叶斯推理、建模和预测技术

• 批准号: 9012328
• 负责人: Arnold Zellner
• 金额: $ 1.4万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-11-15 至 1992-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9012328&HistoricalAwards=false
• 关键词: Bayesian; Inference; Modeling; Forecasting; Techniques

This project continues research on producing improved and new Bayesian econometric methods and applying them in analyzing and predicting economic data. Topics to be pursued include: (1) work on Bayesian learning models and procedures for generating prior densities and models for observations; (2) Bayesian model and forecast combining techniques with applications; and (3) macroeconomic theory and Bayesian macroeconometric forecasting equations. This research should improve existing methods of econometric inference and estimation. The theoretical derivations of information processing rules could lead to a powerful new framework for analyzing statistical information. The Bayesian diagnostic checking procedures developed in this project will prove useful in detecting outliers and departures from specifying assumptions in econometric studies. A predictive test of when to combine and when not to combine forecasts developed under the previous NSF grant will be generalized and applied to economic problems.

该项目继续研究生产改进的和新的 贝叶斯计量经济学方法及其在分析和 预测经济数据。 讨论的主题包括：（1）工作 关于贝叶斯学习模型和程序， 观测的密度和模型;（2）贝叶斯模型和 预测技术与应用相结合;（3） 宏观经济理论与贝叶斯宏观经济计量预测 方程 这项研究将改进现有的计量经济学方法， 推理和估计。 的理论推导 信息处理规则可能会产生一个强大的新框架 用于分析统计信息。 贝叶斯诊断 在这个项目中开发的检查程序将被证明是有用的 在检测异常值和偏离特定假设时， 在计量经济学研究中。 何时将联合收割机和 何时不结合根据以前的NSF制定的联合收割机预测 赠款将被推广并应用于经济问题。