Bayesian Inference and Econometric Modeling

贝叶斯矩方法（Bayesian Method of Moments，简称BMF）是一种统计和计量经济学估计、预测和模型制定的方法，它允许研究人员在不需要传统贝叶斯分析所需的分布假设的情况下获得参数估计。 本研究计划的目标是（1）进一步发展和提供贝叶斯方法的其他应用;（2）比较贝叶斯方法和传统贝叶斯及非贝叶斯推理技术，并开发在贝叶斯方法和传统贝叶斯结果之间进行选择和/或组合的程序;以及（3）在应用模型制定和预测应用中利用BMOM和其他程序，涉及多变量动态统计和计量经济学模型有助于了解过去的经验，并在点和转折点的预测。 应用程序将涉及建模和预测18个或更多工业化经济体的产出增长率，并分析美国区域石油市场。 过去的经验表明，预计采用这种方法的新经验将表明，这种方法是可行的，比以前的方法更简单，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.