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30661-01 - Vorlesung: Advanced Time Series Analysis 3 KP

Semester Frühjahrsemester 2022
Angebotsmuster Jedes Frühjahrsem.
Dozierende Simon Beyeler (simon.beyeler@unibas.ch)
Sylvia Kaufmann (sylvia.kaufmann@unibas.ch, BeurteilerIn)
Inhalt The course introduces multivariate time series modelling, in particular vector autoregression. In a first part, we discuss the model under an analytical viewpoint. We derive properties of the time series process, discuss stationarity and invertibility conditions, derive conditional and unconditional moments, identify sources of uncertainty. As single parameters are not of prime interest, tools like impulse responses and variance decomposition are used to interpret multivariate time series models. Besides improved structural in-sample analysis, multivariate analysis may improve eventually forecasting performance.

Two following parts discuss model estimation from a frequentist and a Bayesian perspective, respectively. A Bayesian approach circumvents estimation difficulties when either data information is scarce (i.e. data series are short) or large (many time series are available). We discuss model choice and specification measures, and procedures to quantify uncertainty.

The last part introduces latent variables into multivariate modelling. These capture latent processes determining the data like regime-switching parameters or underlying common factors.

The course includes exercise sessions.
Lernziele - Analyse and derive the properties of multivariate time series models.
- Perform model specification/comparison; understand and apply tools to interpret model estimates.
- Understand the differences between frequentist estimation and Bayesian inference.
- Implement estimation and various structural identification procedures, quantify uncertainty.
- Estimate basic latent variable models.
- Basic knowledge of forecasting procedures, forecast evaluation.
Literatur Gelman A., Carlin J.B., Stern H.S. and Rubin, D.R. (1995), Bayesian Data Analysis, Chapman and Hall, London.
Greenberg Edward, 2013, Introduction to Bayesian Econometrics, Cambridge University Press, Cambridge UK.
Lütkepohl Helmut, 2005, New Introduction to Multiple Time Series Analysis, Springer.
Neusser Klaus, 2016, Time Series Econometrics, Springer International Publishing AG Switzerland.
Bemerkungen The course will be taught "in class".
Weblink Weblink


Teilnahmebedingungen Completed BA (preferably in economics).
Econometrics MA level; univariate time series modelling (advantageous).

Knowledge of econometrics or programming software (like e.g. EViews, matlab, R)
Anmeldung zur Lehrveranstaltung Registration: Please enrol in MOnA. EUCOR-Students and students of other Swiss Universities have to enrol at the students administration office (studseksupport1@unibas.ch) within the official enrolment period. Enrolment = Registration for the exam!
Unterrichtssprache Englisch
Einsatz digitaler Medien kein spezifischer Einsatz


Intervall 14-täglich
Datum 23.02.2022 – 01.06.2022
Zeit Mittwoch, 14.15-18.00 Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31

The course will be taught "in class".

Datum Zeit Raum
Mittwoch 23.02.2022 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31
Mittwoch 09.03.2022 14.15-18.00 Uhr Fasnachtsferien
Mittwoch 23.03.2022 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31
Mittwoch 06.04.2022 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31
Mittwoch 20.04.2022 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31
Mittwoch 04.05.2022 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31
Mittwoch 18.05.2022 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31
Mittwoch 01.06.2022 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31
Module Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät)
Modul: Field Electives in Economics and Public Policy (Masterstudium: Economics and Public Policy)
Modul: Specific Electives in Data Science and Computational Economics (Masterstudium: Wirtschaftswissenschaften)
Modul: Statistik und Computational Science (Masterstudium: Actuarial Science)
Spezialisierungsmodul: Areas of Specialization in International and/or Monetary Economics (Masterstudium: International and Monetary Economics)
Vertiefungsmodul: Quantitative Methods (Masterstudium: Wirtschaftswissenschaften (Studienbeginn vor 01.08.2021))
Leistungsüberprüfung Leistungsnachweis
Hinweise zur Leistungsüberprüfung 40% Two assignments (team work of 3-5 persons)
60% Written exam (open book): 23.06.22; 14:30-15:30. DSBG Sporthalle: A-Z.

The exam is scheduled in presence. If entrance checks are necessary for the exam due to Covid protection measures, the start of the exam may still be delayed by up to 30 minutes.
In case COVID-19 protective measures prevent examination on site, the faculty reserves the right to conduct the examination electronically during the same time slot.

An-/Abmeldung zur Leistungsüberprüfung An-/Abmelden: Belegen resp. Stornieren der Belegung via MOnA
Wiederholungsprüfung keine Wiederholungsprüfung
Skala 1-6 0,1
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch
Anbietende Organisationseinheit Wirtschaftswissenschaftliche Fakultät / WWZ