| Dozent/in |
Dr. rer. oec. Gregor Bäurle; Dr. rer. oec. Andreas Bachmann |
| Veranstaltungsart |
Workshop |
| Code |
FS261061 |
| Semester |
Frühjahrssemester 2026 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Di, 17.02.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 24.02.2026, 08:15 - 10:00 Uhr, HS 12
Di, 03.03.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 10.03.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 24.03.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 31.03.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 14.04.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 21.04.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 28.04.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 05.05.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 12.05.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 19.05.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214
Di, 26.05.2026, 08:15 - 10:00 Uhr, Inseliquai 10 INE 214 |
| 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 |
Binding registration takes place via the UniPortal; see notes below in the «Proof of Performance» field.
For course information and materials, registration on the OLAT e-learning platform is required from 2 – 15 February 2026. The students themselves are responsible for verifying the course’s creditability towards their degree program.
Direct link to the OLAT course: to follow
|
| Leistungsnachweis |
***IMPORTANT*** In order to acquire credits, and/or to take the examination, registration via the Uni Portal between 2 - 15 February 2026 is MANDATORY. 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 |