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Bayesian Econometrics


  • The course is ususally offered in summer term.
  • In addition to the lecture, practices are offered during which problems from the lectures are being discussed and solved.
  • Please use KLIPS2.0 for finding information on dates of lectures and practices.

Contents and objectives

The students understand advanced, specialized theories and methods in Statistics and Econometrics to analyse current questions and challenges. They collect and analyse data material for selected scientific questions using statistical and econometric methods. Furthermore they justify and defend (independently developed) positions or problem solutions and discuss scientific topics in a professional manner and appropriate to the situation with specialists. The students use techniques of scientific work and good scientific practice.

Module contents include:

  • Basic Principles of Bayesian Econometrics
  • Bayesian Estimators and Numerical Integration
  • Importance Sampling and Markov-Chain-Monte-Carlo
  • Gaussian Linear Regression Model with Conjugate Priors
  • Gaussian Linear Regression Model with Non-Conjugate Priors
  • Linear Regression Model with General Error Covariance Matrix
  • Time Series Models
  • Models for discrete dependent variables
  • Students will practice the use of the methods using econometric software to analyse economic data