The course provides an introduction to Bayesian econometrics. The first part of the course is mainly concerned with introducing the students to the Bayesian paradigm applied to simple regression models. Specifically, the first set of lectures deal with the Bayesian analysis of the linear regression model under conjugate- and non-conjugate priors. The second part of the course deals with recent advances in variable selection through shrinkage priors. During all classes, special emphasis is paid to designing simple algorithms to carry out estimation and inference in R in an on-line basis. Specifically, students are expected to bring their own laptops in order to follow the coding sessions.

This course introduces the students to basic concepts in Bayesian econometrics with special emphasis on implementing and constructing simple Markov chain Monte Carlo (MCMC) algorithms in R. Upon completing this course students should be able to apply the concepts discussed in class to their own PhD projects.

Lectures that provide a detailed introduction to the econometric concepts as well as interactive coding sessions.

- Active participation in class + small weekly assignments (20%)

- A term paper (50%)

- End-term presentation (30%)

- Basic knowledge in frequentist econometrics and statistics

- Basic knowledge of R (see http://tryr.codeschool.com for a quick catch up)

- Students are advised to bring their own laptops to class

fhuber@wu.ac.at

Supplementary Literature

Lancaster, T. (2004). Introduction to Modern Bayesian Econometrics, Wiley-Blackwell.

Kruschke, J.K. (2010). Doing Bayesian Data Analysis: A Tutorial with R and Bugs.