Detailed course description:

• Short recap of VARs
• Bayesian linear regression
• Marginal likelihood. hierarchical priors.
• The independent/conjugate normal-inverse Wishart prior, Litterman/Jeffrey’s/Zellner’s prior, Theil mixed estimator
• Gibbs sampling, Monte Carlo sampling, corvengence, mixing
• Carter-Kohn algorithm, The Kim, Shepard, and Chib algorithm, Metropolis-Hastings algorithms,
• VARs with time-varying volatilities.
• Large Bayesian VARs, Stochastic search variable selection
  Markov-switching model, Hamilton Filter, MS-VAR, panel MS-VAR
• Bayesian matrix autogregressive model (BMAR)
• Unobserved component models
• Bayesian Nonparametric, Dirichlet and Pitman-Yor process priors, finite and infinite mixture, mixture mixed VAR
• Point, density forecasts, fan charts, evaluation of forecasts
• Bayesian model averaging, density forecast combinations

Practical classes are taught in Matlab. Participants are asked to install the software before the course.

28.01.2025 – 31.01.2025
15:00-21:30 GMT+1(Warsaw, Madrid, Berlin, Rome)
9:00-15:30 EST (Boston, New York, Washington)
24 hours contact hours