Bayesian Inference and Econometric Modeling

该项目继续发展和应用贝叶斯统计 解决重大经济问题的方法。 过去的工作 研究人员开发了许多强大的分析工具， 广泛应用于几乎所有的理论和实证领域 经济学研究。 目前的项目强调寻找 明确的，可重复的程序， 观测，组合模型的信息处理规则 和其他信息，以及评估替代方案的程序 模型使用数据。 这项新研究的一个贡献来自于 从形式化当前构建经济模型的实践 并以一种可重复的方式评估它们， 科学严谨。 这些方法的初步工作 表明贝叶斯方法可以显著地提供 美国季度宏观经济预测有所改善 宏观经济数据和欧洲数据。 提高 经济预测的科学严谨性和经验准确性 是全球变化经济学倡议的一个目标。 为 例如，该项目正在开发的方法正在 用于联合收割机和评估非常不同的预测， 气候变化的四个主要模型。 该项目的主要目标是提供一个统一的贝叶斯- 计量经济学推理和建模问题的Maxent方法 并检查其在两个应用领域的性能，宏观， 经济计量建模和预测与生产函数 分析. 在贝叶斯-马克森特方法中， 以及它们的相关先验密度作为解导出， 使用熵的显式、约束、优化问题 理念的 此外，联合收割机 先验密度和似然函数，例如贝叶斯 定理，也导出了约束极大值问题的解 问题 研究进一步表征并扩展 贝叶斯-马克森特方法的适用性。

This project continues to develop and apply Bayesian statistical methods to important economic problems. Past work by the investigator yielded many powerful analytical tools that are now widely used in almost every area of theoretical and empirical research in economics. The current project emphasizes finding explicit, reproducible procedures for producing models for observations, information-processing rules for combining models and other information, and procedures for evaluating alternative models using data. One contribution of this new research comes from formalizing current practice in constructing economic models and evaluating them in a way that is reproducible and scientifically rigorous. Preliminary work with these methods shows that the Bayesian approach can provide dramatically improved macroeconomic forecasts from U.S. quarterly macroeconomic data and from European data. Improving the scientific rigor and the empirical accuracy of economic forecasts is a goal of the economics of global change initiative. For example, the methods being developed by this project are being used to combine and evaluate the very different forecasts from the four primary models of climate change. The main objective of this project is to provide a unified Bayes- Maxent approach to econometric inference and modeling problems and examine its performance in two areas of application, macro- econometric modeling and forecasting and production function analysis. In the Bayes-Maxent approach, models for observations and their associated prior densities are derived as solutions to explicit, constrained, optimization problems using entropy concepts. Further, information-processing rules which combine prior densities and likelihood functions, for example Bayes's Theorem, are also derived as solutions to constrained maxent problems. Research to characterize further and extend the applicability of the Bayes-Maxent approach are pursued.