Applied Bayesian Methods

Catalog description

This course provides a detailed coverage of Bayesian inferential methods and their applications to a variety of problems drawn from economics and business. Starting with basic concepts of probability and inference, the treatment covers prior and posterior distributions, classical and MCMC simulation methods, regressions for univariate and multivariate outcomes, computation of the marginal likelihood and model choice, and estimation of dynamic stochastic general equilibrium (DSGE) models. The key learning objective is for students to develop hands-on Bayesian skills required to conduct data analysis useful for economic and financial decision making.