| Dozent/in |
Dr. rer. oec. Gregor Bäurle |
| Veranstaltungsart |
Workshop |
| Code |
FS221005 |
| Semester |
Frühjahrssemester 2022 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Do, 03.03.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 10.03.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 17.03.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 24.03.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 31.03.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 07.04.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 14.04.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 28.04.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 05.05.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 12.05.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 19.05.2022, 08:15 - 10:00 Uhr, 4.B01
Do, 02.06.2022, 08:15 - 10:00 Uhr, 4.B01 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
weekly |
| Inhalt |
This workshop covers various topics on constructing and evaluating forecasts in economics and business. This includes 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 packages R and partly in EViews; introductions are given during the course. |
| Lernziele |
Students learn how to use time-series models for forecasting. This includes 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 is a prerequisite. Knowledge in time-series analysis, as taught in the lecture “Analysing and forecasting economic time series” (fall 2021) is highly recommended, but not required. Furthermore, prior knowledge of R or similar statistics programs is not a strict prerequisite, but highly recommended. |
| Sprache |
Englisch |
| Begrenzung |
The maximum number of participants is restricted to 20. |
| Anmeldung |
To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 7 February to 4 March 2022. 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/17168236549 |
| Leistungsnachweis |
***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 |
Individual / group presentation / 3 Credits
|
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
gregor.baeurle@snb.ch |
| Anzahl Anmeldungen |
9 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 |