Bayesian Inference and Econometric Modeling

该项目继续开发贝叶斯统计方法并将其应用于重要的经济问题。 研究人员过去的工作产生了许多强大的分析工具，这些工具现在广泛应用于经济学理论和实证研究的几乎每个领域。 当前的项目强调寻找明确的、可重复的程序来生成观测模型，组合模型和其他信息的信息处理规则，以及使用数据评估替代模型的程序。 这项新研究的一个贡献来自于将当前构建经济模型的实践形式化，并以可重复且科学严谨的方式对其进行评估。 使用这些方法的初步工作表明，贝叶斯方法可以根据美国季度宏观经济数据和欧洲数据提供显着改进的宏观经济预测。 提高经济预测的科学严谨性和实证准确性是全球变化经济学倡议的目标。 例如，该项目正在开发的方法用于组合和评估四种主要气候变化模型的截然不同的预测。 该项目的主要目标是为计量经济推理和建模问题提供统一的贝叶斯-马克森方法，并检查其在宏观计量经济建模和预测以及生产函数分析两个应用领域的性能。 在 Bayes-Maxent 方法中，观测模型及其相关先验密度是使用熵概念导出的，作为显式约束优化问题的解决方案。 此外，还导出了结合先验密度和似然函数的信息处理规则，例如贝叶斯定理，作为约束最大问题的解决方案。 正在进行进一步表征和扩展 Bayes-Maxent 方法的适用性的研究。

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.