| Dozent/in |
Dr. rer. oec. Gregor Bäurle; Dr. rer. oec. Andreas Bachmann |
| Veranstaltungsart |
Workshop |
| Code |
FS251013 |
| Semester |
Frühjahrssemester 2025 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Do, 20.02.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 06.03.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 13.03.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 20.03.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 27.03.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 03.04.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 10.04.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 17.04.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 01.05.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 08.05.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 15.05.2025, 08:15 - 10:00 Uhr, 4.B51
Do, 22.05.2025, 08:15 - 10:00 Uhr, 4.B51 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
weekly |
| Inhalt |
This workshop covers various topics on constructing and evaluating forecasts in economics and business. This includes preparing the data, model specification and selection, modelling forecast uncertainty, evaluation of forecast performance and combining models in order to optimize forecasting performance. A particular focus is given to the presentation and communication of forecasts. While the main goal of the workshop is that students apply these skills to their own forecasting project, fundamental theoretical concepts are taught in class together with examples of real-world applications. The applications will be presented in the software package R. |
| Lernziele |
Students learn how to implement time-series models for forecasting in practice. This includes preparing the data, model specification and selection, modelling forecast uncertainty, evaluation of forecast performance and combining models in order to optimize forecasting performance. Students understand both the underlying theoretical concepts and are able to implement these concepts to real world forecasting problems. They are able to communicate the results efficiently. |
| Voraussetzungen |
Solid knowledge in statistics and econometrics as well as knowledge of R or similar statistics programs are a prerequisite. Knowledge in time-series analysis, as taught in the lecture “Analysing and forecasting economic time series”, is highly recommended but not strictly required. |
| Sprache |
Englisch |
| Begrenzung |
Max. 20 participants |
| Anmeldung |
To attend the course /
exercise, registration via e-learning platform OLAT is required. Registration
is possible from 3 – 28 February 2025. The students themselves are
responsible for checking the creditability of the course to their course of
study.
Direct link to OLAT course: https://lms.uzh.ch/url/RepositoryEntry/17654940198 |
| Prüfung |
***IMPORTANT*** In order to acquire credits, resp. to take the examination, registration via the Uni Portal within the examination registration period is ESSENTIALLY REQUIRED. Further information on registration: www.unilu.ch/wf/pruefungen |
| Abschlussform / Credits |
Written paper, individual / group presentation / 3 Credits
|
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
gregor.baeurle@snb.ch / andreas.bachmann@doz.unilu.ch |
| Anzahl Anmeldungen |
0 von maximal 20 |
| Literatur |
Selected parts of Klaus Neusser’s “Time Series Econometrics” (2016), to be downloaded free of charge from https://link.springer.com/book/10.1007%2F978-3-319-32862-1)
Selected parts of Frank Diebold’s “Forcasting in Economics, Business, Finance and Beyond” (2017), to be downloaded free of charge from https://www.sas.upenn.edu/~fdiebold/Textbooks.html |