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Bayesian Econometrics

  • Teacher(s)
    Richard Paap
  • Research field
  • Dates
    Period 2 - Oct 30, 2023 to Dec 22, 2023
  • Course type
  • Program year
  • Credits

Course description

Bayesian Econometrics plays an important role in quantitative economics, marketing research and finance. This course discusses the basic tools which are needed to perform Bayesian analyses. It starts with a discussion on the difference between Bayesian and frequentist statistical approach. Next, Bayesian parameter estimation, forecasting and Bayesian testing is considered, where we deal with univariate models, multivariate models and panel data models (Hierarchical Bayes techniques). To perform a Bayesian analysis, knowledge of advanced simulation methods is necessary. Part of the course is devoted to Monte Carlo integration using Importance Sampling and MCMC methods like Gibbs sampling, Metropolis-Hasting sampling, Slice sampling and Hamiltonian MC sampling, The course ends with variational Bayesian inference which allows for fast approximate posterior inference. The topics are illustrated using simple computer examples in Python which are demonstrated during the lectures.


Advanced Econometrics (I, II)

Course literature

Primary reading
- Slides provided during the lecture.
- Greenberg, E. (2013). Introduction to Bayesian Econometrics, Cambridge University Press, 2nd edition.
- Selected papers.