Bayesian Inference, Modeling and Forecasting Techniques in Econometrics

该项目继续研究改进的和新的贝叶斯计量经济学方法，并将其应用于分析和预测经济数据。要研究的主题包括：(1)研究贝叶斯学习模型和为观测生成先验密度和模型的程序；(2)结合技术和应用的贝叶斯模型和预测；(3)宏观经济理论和贝叶斯宏观计量经济学预测方程。这项研究应该改进现有的计量经济推断和估计方法。信息处理规则的理论推导可能导致分析统计信息的一个强大的新框架。本项目中开发的贝叶斯诊断检查程序将被证明在经济计量学研究中检测离群值和偏离特定假设的情况下是有用的。对何时合并和何时不合并根据前一次NSF拨款开发的预测进行的预测测试将推广并应用于经济问题。

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.