Bayesian macroeconometrics
A PhD-level course. Practical classes are taught in MATLAB. Participants are asked to install the software before the course.
- Software
- MATLAB
- Contact hours
- 24 hours
- Previous edition
- 28–31 January 2025, daily 15:00–21:30 GMT+1 (Warsaw, Madrid, Berlin, Rome), 9:00–15:30 EST (Boston, New York, Washington)
- Next edition
- Dates will be announced on the PhD courses page.
Detailed course description
- Short recap of VARs
- Bayesian linear regression
- Marginal likelihood, hierarchical priors
- The independent/conjugate normal-inverse Wishart prior, Litterman/Jeffreys/Zellner’s prior, Theil mixed estimator
- Gibbs sampling, Monte Carlo sampling, convergence, mixing
- Carter–Kohn algorithm, the Kim, Shephard and Chib algorithm, Metropolis–Hastings algorithm, Kalman filter
- VARs with time-varying volatilities
- Large Bayesian VARs, stochastic search variable selection
- Markov-switching model, Hamilton filter, MS-VAR, panel MS-VAR
- Bayesian matrix autoregressive model (BMAR)
- Unobserved component models
- Bayesian nonparametrics: Dirichlet and Pitman–Yor process priors, finite and infinite mixture, mixture mixed VAR
- Point and density forecasts, fan charts, evaluation of forecasts
- Bayesian model averaging, density forecast combinations
Registration and fees
After registration, each participant receives an email with an invoice and payment details. Discounts and the cancellation policy are described on the payments page. Questions: contact@slawomirkonopa.com.