This course develops an understanding of the Bayesian paradigm and its application in macroeconometrics with strong focus on stateoftheart models in empirical macroeconomics.
The course is divided into three blocks, namely a theoretical and a computational part, and finally, a session discussing stateofthe art models. We will start with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and seeing how Bayesian methods work in the familiar context of simple regression models. In addition, as computational methods are of great importance in Bayesian econometrics we will discuss them in detail. Subsequently, the course turns to state space models and discusses their estimation challenges. These include time series models where parameters change over time, models with regime change and stochastic volatility models.
Hence, several topics and the empirical application of the flexible models will be covered (including programming sessions in R) such that students gain an overview of today's most recent methods. The main topics, inter alia, cover the linear (multivariate) regression model (under the Bayesian paradigm), Statespace models (Kalman filter), and BVARs. We will focus on sampling methods which allows us to estimate this large class of models (Bayesian computation).
Syllabus

LVTyp
PI 
Semesterstunden
2 
Unterrichtssprache
Englisch
Wochentag  Datum  Uhrzeit  Raum 

Freitag  01.03.2019  13:00  14:30  TC.4.18 
Freitag  08.03.2019  13:00  14:30  TC.4.18 
Freitag  15.03.2019  13:00  14:30  TC.4.18 
Freitag  22.03.2019  13:00  14:30  TC.4.18 
Freitag  29.03.2019  13:30  15:30  D4.0.022 
Freitag  05.04.2019  13:00  14:30  TC.4.18 
Freitag  12.04.2019  13:00  14:30  TC.4.18 
Freitag  03.05.2019  13:00  14:30  TC.4.18 
Freitag  10.05.2019  13:00  14:30  TC.4.18 
Freitag  17.05.2019  13:00  14:30  TC.4.18 
Freitag  24.05.2019  13:00  14:30  TC.4.18 
Freitag  31.05.2019  13:00  14:30  TC.4.18 
Freitag  07.06.2019  13:00  14:30  TC.4.18 
Freitag  14.06.2019  13:00  14:30  TC.4.18 
Freitag  21.06.2019  13:00  14:30  TC.4.18 
Freitag  28.06.2019  13:00  14:30  TC.4.18 
The course will be helpful for students interested in working in academic positions and publishing their work in scientific journals. Moreover, students will gain sufficient knowledge about Bayesian computation and programming so that they are able to independently combine the covered topics to estimate their own models in R. Finally, the students can demonstrate their acquired skills by writing own research papers. Excellent papers may be extended to a Master thesis.
Attendance is mandatory (however, three missed units are tolerated)
This lecture consists of three main blocks. While the first block is dedicated to a theoretical discussion of Bayesian econometrics, the second block covers computational issues and there implementation in the software package \textbf{R}. The third block gives a dense treatment of the current research frontier of highdimensional problems and forecasting challenges.
Students carry out a group (max. 5 students) research project by applying the discussed tools and models to a macroeconomic question of choice. The paper should be structured and written according to the guidelines of Economics Letters and should not exceed about 10 pages in length (including: a separate title page with an abstract summarizing the paper; a complete list of references; excluding: a list of data sources, R code). The paper should be explicit enough for a fellow student to be able to replicate all results (therefore, data sources must be documented and modelling choices should be defended). You should clearly explain what the research question is, why the question is interesting, and what you have learned. For the paper you need to conduct an estimation by yourself implemented in R. The final paper that is due at the end of the term (01. July 2019) has to be uploaded at the learn@wu assignment tool for a plagiarism check.
Please make sure that you read the assigned literature PRIOR to the lecture. Each lecturer will cover a more or less closed topic.
Final Exam (50Points), Exercises (20Points) and Research Paper (30Points).
A positive final test (50% threshold of total exam points) is required for passing the course.
Grading Key: <60Points: fail; >61Points: sufficient; >71Points: satisfactory; >81points: good; >91Points: very good.
Students should have a sound knowledge of statistics (probability, random variables, expectations, joint/conditional distributions), mathematics (linear algebra, differential/integral calculus, algebra) and basic econometrics (OLS/ML estimation).
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