Syllabus

Titel
6048 Advanced Macroeconometrics (Science Track)
LV-Leiter/innen
Niko Hauzenberger, MSc (WU), Michael Pfarrhofer, MSc (WU), Dr. Thomas Zörner, BA, MSc (WU)
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
07.02.2019 bis 17.02.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Termine
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

Inhalte der LV

This course develops an understanding of the Bayesian paradigm and its application in macroeconometrics with strong focus on state-of-the-art models in empirical macroeconomics.
The course is divided into three blocks, namely a theoretical and a computational part, and finally, a session discussing state-of-the 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), State-space models (Kalman filter), and BVARs. We will focus on sampling methods which allows us to estimate this large class of models (Bayesian computation).

Lernergebnisse (Learning Outcomes)

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.

Regelung zur Anwesenheit

Attendance is mandatory (however, three missed units are tolerated)

Lehr-/Lerndesign

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 high-dimensional 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.

Leistung(en) für eine Beurteilung

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.

Empfohlene inhaltliche Vorkenntnisse

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).

Zuletzt bearbeitet: 07.01.2019



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