Universität Wien
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040057 KU 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

  • Friday 03.03. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 07.03. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 10.03. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 14.03. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 17.03. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 21.03. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 24.03. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 28.03. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 31.03. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 18.04. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 21.04. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 25.04. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 28.04. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 02.05. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 05.05. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 09.05. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 12.05. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 16.05. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 19.05. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 23.05. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 26.05. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 02.06. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 06.06. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 09.06. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 13.06. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 16.06. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 20.06. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 23.06. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 27.06. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 30.06. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock

Information

Aims, contents and method of the course

The course focuses on econometric methods used in applications to aggregate macroeconomic data. The course considers time series analysis from a Bayesian perspective and consists of the following main building blocks:

(0) Necessary mathematical concepts and classical econometrics (recap)
(1) Univariate time series
(2) Multivariate time series
(3) Introduction to Bayesian econometrics
(4) State-space models
(5) Structural and predictive inference

The course aims at deepening the understanding of econometric methods that are useful in the analysis of macroeconomic (time series) data. By the end of the course, students are expected to have acquired a good understanding of how to analyze univariate and multivariate time series and how to apply this knowledge to macroeconomic data.

Assessment and permitted materials

The evaluation consists of three components: midterm take-home exam (20%), final take-home exam (30%), and an empirical project (50%). The empirical project consists of writing a short paper (40%), presenting own results (10%) and discussing the results of fellow students. The take-home exams are open book, that is, students may use any materials or software if they are properly referenced. Each task must be handed in as a single PDF file. There will also be sporadic homework that may be solved at home and presented in class for bonus points.

Minimum requirements and assessment criteria

There are no "official" mandatory preliminary requirements for taking this class. It will be beneficial to have prior knowledge of linear algebra and probability & statistics, alongside classical econometrics and statistical software. We will, however, revisit all required concepts at the beginning of the semester. 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 exams is defined by what has been taught in the course. Students are asked to prepare an empirical project that is related to the course contents, and to present and discuss their results during the last weeks.

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).
– Hamilton, J.D.: "Time Series Analysis" (Princeton University Press).
– Kim, J.C and Nelson, C.R.: "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" (MIT Press).

Relevant additional research papers or materials will be made available over the course of the lecture.

Association in the course directory

Last modified: Mo 08.05.2023 15:26