Bayesian Inference and Econometric Modeling

贝叶斯矩方法(BMOM)用于统计和计量经济学估计、预测和模型制定，允许研究人员在没有传统贝叶斯分析所需的分布假设的情况下获得参数估计。本研究项目的目标是：(1)进一步发展和提供BMOM方法的更多应用；(2)比较BMOM与传统贝叶斯和非贝叶斯推理技术，并制定在BMOM和传统贝叶斯结果之间进行选择和/或组合的程序；以及(3)在涉及多变量动态统计和计量经济学模型的应用模型制定和预测应用中利用BMOM和其他程序，这些模型有助于了解过去的经验，并在时间点和转折点预测中发挥作用。应用程序将涉及对18个或更多工业化经济体的产出增长率进行建模和预测，以及对美国地区石油市场的分析。过去的经验表明，预计生物武器管理办法的新经验将表明，它是可操作的，比以前的办法更简单，并产生非常令人满意的结果。

The Bayesian Method of Moments (BMOM) approach to statistical and econometric estimation, prediction, and model formulation allows to the researcher to obtain parameter estimates without the distributional assumptions required for traditional Bayesian analysis. The objectives of this research project are (1) to develop further and provide additional applications of the BMOM approach; (2) to compare BMOM and traditional Bayesian and non-Bayesian inference techniques and develop procedures for choosing between and/or combing BMOM and traditional Bayesian results; and (3) to utilize BMOMs and other procedures in applied model formulation and forecasting applications involving multivariate dynamic statistical and econometric models useful in gaining understanding of past experience and in point and turning point forecasting. Applications will involve modeling and forecasting output growth rates for 18 or more industrialized economies and analyses of U.S. regional petroleum markets. Past experience has indicated and it is anticipated that new experience with the BMOM approach will indicate that it is operational, simpler than previous approaches, and yields very satisfactory results.