Mathematical Sciences: Conference on Forecasting, Predictionand Modeling in Statistics and Econometrics: Bayesian and Non-Bayesian Approaches

数学科学：统计和计量经济学中的预测、预测和建模会议：贝叶斯和非贝叶斯方法

• 批准号: 9409774
• 负责人: Arnold Zellner
• 金额: $ 1.88万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-07-01 至 1995-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9409774&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Conference; Forecasting; Predictionand

Good procedures for formulating quantitative models that are useful in explanation, prediction and decision-making are sorely needed in all sciences and applied areas. Currently, model formulation is more of an art than a science with much disagreement about how to approach the problem. Some emphasize simplicity, parsimony, and Ockham's razor while others emphasize the need for complexity, detail and realism. A number of approaches have been put forward that have been used in practice, namely, method of moments, time-series "identification" procedures using autocovariance matrices, etc., encompassing methods, structural econometric modeling-time series analysis procedures, maximum entropy, quantum statistical inference and so on. Further, the roles of measurement, description, unusual and ugly facts in model formulation have to be considered. Papers presented at the Geisser Conference will consider these difficult issues in detail, present and compare Bayesian and non-Bayesian approaches, and incorporate analyses of data to illustrate the results of applying various methods. A summary of the Conference's research findings will be prepared. Forecasting, prediction and modeling are central theoretical and applied topics in Statistics and Econometrics. Time series forecasting methods that are widely employed in industry and government yield useful forecasts of future developments but little in the way of explanation. On the other hand, causal models can provide predictions and explanations of future developments and how they may be influenced by various policies. Improving procedures for developing, implementing and using forecasting and causal models is a major objective of this Conference. In this connection, a comparative evaluation of Bayesian and non-Bayesian methods for achieving the above objective will be provided using many applications to illustrate general points and evaluate alternative approaches. Thus the Conference will provide evaluations and applications of old and new procedures for developing, implementing and using forecasting and causal models.

所有科学和应用领域都迫切需要建立可用于解释、预测和决策的定量模型的良好程序。目前，模型制定更像是一门艺术，而不是一门科学，对于如何解决问题存在很大分歧。有些强调简单、简约和奥卡姆剃刀，而另一些则强调复杂性、细节和现实主义的需要。人们提出了许多已在实践中使用的方法，即矩量法、使用自协方差矩阵的时间序列“识别”程序等，包括方法、结构计量经济学建模-时间序列分析程序、最大熵、量子统计推断等。此外，还必须考虑模型制定中测量、描述、异常和丑陋事实的作用。在盖瑟会议上提交的论文将详细考虑这些难题，介绍和比较贝叶斯和非贝叶斯方法，并结合数据分析来说明应用各种方法的结果。将编写会议研究结果的摘要。预测、预测和建模是统计和计量经济学的核心理论和应用主题。工业界和政府广泛采用的时间序列预测方法可以对未来发展做出有用的预测，但几乎无法解释。另一方面，因果模型可以提供对未来发展及其如何受到各种政策影响的预测和解释。改进开发、实施和使用预测和因果模型的程序是本次会议的主要目标。在这方面，将使用许多应用程序对实现上述目标的贝叶斯和非贝叶斯方法进行比较评估，以说明一般要点并评估替代方法。因此，会议将对开发、实施和使用预测和因果模型的新旧程序进行评估和应用。