Bayesian Inference, Modeling and Forecasting Techniques in Econometrics

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This project continues research on producing improved and new Bayesian econometric methods and applying them in analyzing and predicting economic data. Topics to be pursued include: (1) work on Bayesian learning models and procedures for generating prior densities and models for observations; (2) Bayesian model and forecast combining techniques with applications; and (3) macroeconomic theory and Bayesian macroeconometric forecasting equations. This research should improve existing methods of econometric inference and estimation. The theoretical derivations of information processing rules could lead to a powerful new framework for analyzing statistical information. The Bayesian diagnostic checking procedures developed in this project will prove useful in detecting outliers and departures from specifying assumptions in econometric studies. A predictive test of when to combine and when not to combine forecasts developed under the previous NSF grant will be generalized and applied to economic problems.