|Dimitris Korobilis is Professor of Econometrics at the Adam Smith Business School, University of Glasgow and senior fellow of the Rimini Center for Economic Analysis. He works in the field of applied macroeconomics and finance, and applied econometrics. He has worked as consultant for (among others) the European Central Bank, the South African Reserve Bank, and the Scottish Government, and his forecasting models have been adopted by numerous international organizations. Dimitris has published in Journal of Econometrics, Review of Economics and Statistics, and International Economic Review, and he serves as an Associate Editor for Studies in Nonlinear Dynamics and Econometrics.|
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:
- 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)
- Estimate models with more parameters that observations, be this a simple linear regression or a more complex multivariate model
- Compute parameters using a variety of traditional (e.g. MCMC) as well as machine learning algorithms (e.g. variational Bayes)
- Devise new models and algorithms in order to tackle novel empirical problems
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.