Course Description

Bayesian Machine Learning Methods for Macroeconomic and Financial Time Series

Instructor: Dimitris Korobilis, Professor of Econometrics, Adam Smith Business School, University of Glasgow, UK

Course aims and overview

The main aim of this course is to help develop an understanding of Bayesian methods relevant for the analysis of modern financial and macroeconomic time series. The emphasis throughout this course is on Bayesian estimation and computation, with emphasis on flexible modelling and machine learning inference for high-dimensional cases.

This short course will introduce a very large spectrum of time series models used in macroeconomics and finance. Instead of focusing on the theoretical time-series properties of these popular models, we will delve deeply into estimation issues which are of practical importance for applied researchers and PhD students.

By the end of this course the student should be able to:

  1. Specify flexible regression models that account for nonlinearities, stochastic volatility, or models that allow flexible modelling of the whole density of the data (quantile regression; density regression)
  2. Estimate models with more parameters that observations, be this a simple linear regression or a more complex multivariate model
  3. Compute parameters using a variety of traditional (e.g. MCMC) as well as machine learning algorithms (e.g. variational Bayes)
  4. Devise new models and algorithms in order to tackle novel empirical problems
Li Yijie , ASSEE 2017

The whole program reaches an excellent balance!

I learnt a lot about duration analysis, including both theories and how to do empirical analysis with it. The lab sessions are very helpful in improving my understandings of the contents of the lectures. The course is great, and the dinners and excursions are also great.


This course is appropriate for students who already possess experience in time-series methods, and they want to take their skills to the next level. The ideal profile is PhD students in applied macro/finance who want to enhance their research potential by adding new tools in their toolbox, or interns and researchers in central banks (or large organizations) who want to monitor large panels of data in a time series context.

Undergraduate-level knowledge of econometrics is essential, and any further knowledge of econometrics/statistics/probabilistic data science, would be beneficial. We will need to rely heavily on distributions such as the Normal, Bernoulli, Gamma, and Wishart so students should be familiar with the concept of a p.d.f., a c.d.f, and understand (but not remember by heart) their basic functional forms.

Computations are in MATLAB. I will provide all the code in a very accessible form, so that even colleagues with no knowledge of programming can attend this class. Nevertheless, people who are serious about using Bayesian econometrics in their research, are expected to have at least some basic MATLAB skills (e.g. know how to estimate a regression with OLS using basic commands, i.e. ” >> beta_OLS = X/Y “), although more experienced users will be able to keep up more easily with the fast pace of the course (Note: less experienced programmers will inevitably need to self-study after the course is over with the material that I will provide).

Lecture Outline

Part I: Foundations
  • Day 1:
    • Lecture 1a: An overview of Bayesian Inference; The linear regression model
    • Lecture 1b: Bayesian computation; The Gibbs sampler and Metropolis Hastings algorithms
    • Lab 1: Bayesian computation basics
  • Day 2:
    • Lecture 2a: High dimensional estimation using shrinkage and variable selection
    • Lecture 2b: Efficient computation with hierarchical priors
    • Lab 2: Exercises on linear regression and extensions
Part II: Applications
  • Day 3:
    • Lecture 3a: Vector autoregressions, Part I
    • Lecture 3b: Vector autoregressions, Part II
    • Lab 3: Bayesian VARs for monetary policy
  • Day 4:
    • Lecture 4a: Time-varying parameters and stochastic volatility, Part I
    • Lecture 4b: Time-varying parameters and stochastic volatility, Part II
    • Lab 4: TVP regressions and VARs
  • Day 5
    • Lecture 5a: Bayesian quantile regression
    • Lecture 5b: Factor models
    • Lab 5: Modeling macro

Readings and resources

  • Korobilis, D. and Shimizu, K. (2022). “Bayesian approaches to shrinkage and sparse estimation”, Foundations and Trends® in Econometrics, 11 (4), pp. 230-354.
  • Koop, G. and Korobilis, D. (2010). “Bayesian Multivariate Time Series Methods for Empirical Macroeconomics”, Foundations and Trends® in Econometrics, 3, pp. 267-358.
  • Koop, Gary (2003) Bayesian Econometrics, Wiley.
  • Koop, G., Poirier, D. and Tobias, J. (2007) Bayesian Econometric Exercises, Cambridge University Press
  • Bauwens, L. and Korobilis, D. (2013). “Bayesian Methods”, in Handbook of Research Methods and Applications on Empirical Macroeconomics.


Morning Lectures

Summer school lectures start on Monday until Friday. The lectures are divided into two morning sessions and they take place in Room A2-3 on the ground floor of the Economics Building as follows:

  • Morning Session 1: 09:00-11:00
  • Morning Session 2: 11:30-13:30

Computer Practicums

Computer practicums will take place in the afternoon from Monday to Friday (except Wednesday afternoon) in the Computer Lab which is located next to the Economics Building on the way to Student's Halls and University Campus Restaurant:

  • Afternoon Lab Session: 15:30-17:30

Breakfast, Lunches and Coffee Breaks

Breakfast and lunches will take place in the VIP Room of the University Campus Restaurant with the following schedule:

  • Breakfast: 08:00-09:00 from Monday to Friday
  • Lunch: 14:00-15:00 from Monday to Friday

Two coffee breaks will be available outside the Economics Building between the two morning lectures at 11:00-11:30 and right before the computer practicum at 15:00-15:30 every day from Monday to Friday.

Social Programme

Reflecting ASSEE enterprising approach, alongside the teaching and learning our Summer School also offers a unique programme of social, cultural and evening events. The programme offered is included in the tuition fee and allows students to socialise, relax and have fun through a range of activities. Our programme includes:

  • A  Welcome Reception on Monday evening in a lovely local restaurant.
  • An excursion to Plakias on Wednesday afternoon for swimming and relaxation.
  • Farewel Dinner on Friday evening in a traditional Cretan taverna
  • An excursion to Eleftherna Archaeological Museum (one of the most important Greek museums) and to the ancient city of Eleftherna on Saturday morning.

Apply On-Line

Scroll to Top