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.

在所有科学和应用领域都需要迫切需要制定用于解释，预测和决策的定量模型的良好程序。当前，模型表述更像是一种艺术，而不是科学，对如何解决问题有很多分歧。一些人强调简单，简约和奥克汉姆的剃须刀，而另一些则强调了对复杂性，细节和现实主义的需求。已经提出了许多在实践中使用的方法，即时刻的方法，使用自动助力矩阵等的时间序列“识别”程序，包括方法，包括结构计量计算方法，结构计量计算器建模时间串联分析程序，最大熵，量子统计推断等。此外，必须考虑测量，描述，异常和丑陋的事实的作用。在Geisser会议上发表的论文将详细考虑这些困难问题，呈现和比较贝叶斯和非贝斯尼亚方法，并结合数据分析，以说明应用各种方法的结果。会议研究结果的摘要将准备。预测，预测和建模是统计和计量经济学中的中央理论和应用主题。时间序列的预测方法在工业和政府中广泛采用了对未来发展的有用预测，但在解释方面几乎没有。另一方面，因果模型可以提供对未来发展的预测和解释，以及它们如何受到各种政策的影响。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来说明一般点并评估替代方法，对实现上述目标的贝叶斯和非乘坐方法进行比较评估。因此，该会议将为开发，实施和使用预测和因果模型提供旧和新程序的评估和应用。