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

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.

Mindestanforderungen und Beurteilungsmaßstab

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.

Prüfungsstoff

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.

Literatur

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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Di 09.05.2023 11:06