Event Details

The aim of the Summer School is to provide an overview of cutting-edge research and methodologies in Bayesian econometrics and applied macroeconometrics. Participation in the event is free of charge.

The aim of the Summer School is to provide an overview of cutting-edge research and methodologies in Bayesian econometrics and applied macroeconometrics. Participation in the event is free of charge. This event is supported by the Department of Economics at Lancaster University, the North West Social Science Doctoral Training Partnership, the Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy and Timberlake Consultants.

Course Outline

Bayesian methods are increasingly used in econometrics, particularly in the field of macroeconomics. This is a course in Bayesian econometrics with a focus on the models used in empirical macroeconomics. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and showing how Bayesian methods work in the familiar context of the regression model. Computational methods are of great importance in modern Bayesian econometrics and these are discussed in detail. In light of the Big Data revolution, applied economists often face the situation where the number of variables under consideration is large relative to the number of observations and conventional econometric methods do not work well. We describe various methods that can be used with Big Data in the context of the regression model and emphasize the wider applicability of these methods in other modelling contexts. Subsequently, the course shows how Bayesian methods are used with models which are currently popular in macroeconomics such as Vector Autoregressions (VARs), state space models and extensions of VARs such as time-varying parameter VARs (TVP-VARs) and mixed frequency VARs (MF-VARs).

Programme:

Monday 29th June

10:00 am -12 noon: An Overview of Bayesian Econometrics Bayesian Inference in the Normal Linear Regression Model

2:00 pm - 4:00 pm: Bayesian Methods for Regression Models with Big Data Bayesian VARs

Tuesday 30th June

10:00 am - 12noon: Bayesian State Space Modelling

2:00 pm - 4:00 pm: Regional Nowcasting using a Mixed Frequency VAR

Event Recordings

If you missed the event, or would like to revisit it, please take a look at our recordings below:

• PhD Summer School Recording Part 1
• PhD Summer School Recording Part 2

Contact us

For any questions and further information, please contact Giorgio Motta (g.motta@lancaster.ac.uk) or David Kang (b.kang1@lancaster.ac.uk).