Humboldt-Universität zu Berlin - Wirtschaftswissenschaftliche Fakultät

Reading Course in Bayesian Econometrics

Bayesian methods have become increasingly popular, especially in macroeconomics. The large dimensionality of macro-econometric models and the complexity of modern DSGE models often require the use of prior information and computational algorithms to conduct econometric inference. This course will give an introduction to Bayesian estimation both from a technical and practical point of view. The curriculum will cover basic notions of Bayesian inference and posterior simulators, with applications to regression and state space models. Empirical applications and more advanced topics will be treated in reading groups.

Although the focus of the course is on macro-oriented models, micro-oriented student presentations are encouraged.

This course is tailored towards advanced masters and graduate students in Economics or other related disciplines.


Lecturer: Andreas Tryphonides, Ph.D.

Literature: Selected articles

Preconditions: Students should have basic knowledge of probability, regression, time series (ARMA modeling etc) and scientific programming. Familiarity with modern dynamic macroeconomic models is desirable.

Module: MA Economics: 6 Credits, Module: "Topics in Macroeconomics"

Exam: Term paper


Find this course in Agnes and Moodle.