Lecture Notes

1 Review of Bayesian estimation (link to pdf and code)

Linear Regression Model (LRM) with spherical and non-spherical errors, LRM with Time varying parameters and stochastic volatility.

2 Multivariate models (link to pdf and code)

VAR and panel VAR models, VAR models with skewed errors.

3 Bayesian Markov-switching VAR models (link to pdf and code)

Markov-switching (MS) models and Hamilton Filter, MS-VAR and MCMC methods, Multi-country panel MS-VAR, VAR with MS Stochastic Correlation, Application to macroeconomics (e.g. business cycle) and finance (exchange rates and CDS on sovereign bonds).

Notes on HF (link)

4 Structural Graphical VAR Models (link to pdf and code)

Bayesian Networks and MCMC methods for Graphical Models, Graphical VAR models, Applications to macroeconomics, financial contagion and oil market.

5 Bayesian Nonparametric Models (link to pdf and code)

Bayesian Nonparametric, Dirichlet and Pitman-Yor process priors, Infinite mixture representation, Dependent Pitman-Yor process priors, Slice sampling and MCMC sampling for nonparametric models, Nonparametric VAR models, Application to forecasting: density combination models, Applications to macroeconomics (business cycle) and finance (stock markets).

6 Forecasting with Bayesian multivariate models (link to pdf and code)

Computing point and density forecasts from Monte Carlo draws, Evaluation of forecasts, Applications to macroeconomics (GDP growth, inflation, interest rate and unemployment) and finance (electricity prices and cryptocurrencies).

7 Density forecast combinations (link to pdf, link to code)

Bayesian model averaging, time-varying combination, density forecast combinations, Combinations of large data sets, Parallel computation, Applications to macroeconomics and finance .