Applied Bayesian Statistics

ECON 6930 • Spring 2023 • Saint Louis University

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 and Python skills required to conduct data analysis useful for economic and financial decision making. The course will help prepare students entering doctoral education or starting careers in economics, finance, marketing, operations, accounting, political science, and statistics.

Class Information

Lecture Slides

Acknowledgments: development of these lectures has been greatly benefited from discussions with Siddhartha Chib and Hailong Qian. ChatGPT provides excellent teaching assistance on Python programs.