U.S.- Austria Cooperative Research on Asymptotic Bayesian Analysis and Order Selection

这个奖项支持教授彼得菲利普斯考尔斯 耶鲁大学经济学研究基金会， 与Werner Ploberger教授合作 美国计量经济学和运筹学研究所 奥地利维也纳技术大学。 他们计划 在计量经济学的几个研究领域共同工作。 首先，他们将进行贝叶斯的渐近分析 可能是非平稳时间序列。 还有， 他们将开发广义贝叶斯模型选择 具体涉及秩序问题的标准 参数化方法中的选择。 此外，他们打算 编写计算机软件来实现他们所使用的方法， 制定并将其应用于宏观经济数据集， 美国、奥地利和其他几个国家。 科学界对贝叶斯方法的兴趣急剧增长 近年来，它影响了许多不同的领域。 统计学和计量经济学。 许多计量经济学家发现， 贝叶斯方法特别适合于以下问题： 计量经济学中的时间序列分析与推断 与菲利普斯博士合作探索的领域 和普洛伯格。 他们的一些研究将有助于 改进经济时间序列模型。 另 他们所从事的项目有一个重要的应用， 基于数据模型的假设检验的发展 选择原则。 一些假设相当大 对经济学的兴趣，就像一个 自回归单位根，在不同情况下保持不变 模型 这些调查人员计划利用这个 不变性，以便他们的“贝叶斯模型”标准可以使用 选择一个系统对装置进行明确测试 根假设

This award supports Professor Peter Phillips of the Cowles Foundation for Research in Economics, Yale University, to collaborate with Professor Werner Ploberger of the Institute of Econometrics and Operations Research of the Technical University of Vienna in Austria. They plan to work jointly on several lines of research in econometrics. First, they will carry out asymptotic analysis of Bayesian methods for possibly non-stationary time series. Also, they will develop generalized Bayes model selection criteria with specific reference to problems of order selection in parametric methods. In addition, they intend to write computer software to implement the methods they develop and to apply them to macroeconomic data sets for the USA, Austria, and several other countries. Scientific interest in Bayesian methods has grown sharply over recent years and has affected many different fields of statistics and econometrics. Many econometricians find the Bayesian approach particularly well-suited to problems of time series analysis and inference in econometrics, two areas being explored in the collaboration of Drs. Phillips and Ploberger. Some of their research will contribute to improved modelling of economic time series. The other project they are engaged has an important application in the development of hypothesis tests under data-based model selection principles. Some hypotheses of considerable interest in economics, like that of the presence of an autoregressive unit root, remain invariant across different models. These investigators plan to exploit this invariance so that their "Bayes model" criteria can be used to select a system to conduct an explicit test of the unit root hypothesis.