Reading Course in Bayesian Econometrics

Guest Instructor: 
Andreas Tryphonides (HU Berlin)
Time I: 
Wednesday, 08:00am
Venue I: 
SPA1, R21b
Description: 

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.

Please see the attached syllabus for more detailed information. Registration will take place in the first lecture.

Credits: 
3.00
Program: 
Semester: 
Fall 2016
Affiliation: 
Humboldt-Universität zu Berlin