Bayesian Inference and Econometric Modeling

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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.