Universität Wien
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040062 KU Advanced topics in Macroeconometrics (MA) (2023S)

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 02.03. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 03.03. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 09.03. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 10.03. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 16.03. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 17.03. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 23.03. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 24.03. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 30.03. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 31.03. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 20.04. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 21.04. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 27.04. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 28.04. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 04.05. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 05.05. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 11.05. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 12.05. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 19.05. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 25.05. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 26.05. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 01.06. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 02.06. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 09.06. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 15.06. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 16.06. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 22.06. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 23.06. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 29.06. 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 30.06. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

The course focuses on cutting-edge econometric methods used in applications to aggregate macroeconomic data. It aims at introducing students to research close to the current research-frontier. This involves, but is not limited to, Bayesian time-series analysis and recent machine learning approaches:

(1) Introduction to Bayesian econometrics
(2) High-dimensional multivariate time series analysis
(3) Non-/semiparametric methods and machine learning
(4) New approaches for structural and predictive inference

Besides introducing students to such state-of-the-art techniques, an additional focus is to provide them with the necessary knowledge in statistical software (we will use R) to conduct their own research projects. Students will be provided with theoretical inputs alongside empirical examples. A more extensive reading list featuring recent papers accompanying the class will be made available. These papers will be presented by students to their colleagues and discussed in class.

Assessment and permitted materials

The evaluation consists of three components: one homework (20%; writing a referee report), a take-home exam (30%) and presenting selected papers in the context of macroeconometrics to their fellow students (in groups for two times, 25% each for a total of 50%). The homeworks and take-home exam are open book, that is, students may use any materials or software if they are properly referenced. Each of these tasks must be handed in as a single PDF file.

Minimum requirements and assessment criteria

There are no preliminary requirements for taking this class. However, it is beneficial to have prior knowledge of probability and statistics, econometrics and statistical computation. A positive grade requires 50% of the achievable points.

Examination topics

The course comprises 2 lectures of 1.5h per week covering both theory and empirical examples. Slides and computer code (software: R) are made accessible to participants. The relevant material for the take-home exam is defined by what has been taught in the course.

Reading list

The class materials will in part be based on the following books:

– Chan, J., Koop, G., Poirier, D.J. and Tobias, J.L.: "Bayesian Econometric Methods" (Cambridge University Press).
– Kim, J.C and Nelson, C.R.: "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" (MIT Press).

The full reading list of additional research papers will be made available during our first meeting.

Association in the course directory

Last modified: Tu 09.05.2023 11:06