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Bayesian Methods in Econometrics

This course will provide an introduction to simulation-based methods that are commonly used in microeconometrics. The emphasis will be Bayesian, although we will also contrast posterior analysis with maximum likelihood estimation.Despite the technical content, the course begins with an introduction to the Bayesian paradigm and introduces key concepts and vernacular

This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program. 

Key Skills

The basis and motivation for Bayesian Econometrics.

The distinction between inference and estimation in Bayesian versus classical methods.

How to construct posterior distributions of unknown parameters.

The application of Bayesian methods to problems in microeconometrics. 

By the end of this course, participants should have knowledge and ability to: 

Desired Skills

Engage in abstract thinking by extracting the essential features of complex systems to facilitate problem solving and decision-making.

Communicate and present complex arguments in oral and written form with clarity and succinctness.

Appreciate instances where the application of Bayesian methods is appropriate.

Work with R and/or python to operationalise Bayesian methods. 

By the end of this course, students should be able to: 

Structure

 

Bayesian Methods in Econometrics is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.

 

The total number of contact hours is 15 hours. This leaves 85 hours for private study.

Lectures

 

 

 

This module consists of 2- hour lectures per day, for 5 consecutive days, plus a 30 minutes tutorial per day.

During their private study, students should read the course literature, work on practical exercises, and write a project report for the examination.