Bayesian Inference and Econometric Modeling

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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.