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| Semester | spring semester 2022 |
| Course frequency | Every spring sem. |
| Lecturers |
Simon Beyeler (simon.beyeler@unibas.ch)
Sylvia Kaufmann (sylvia.kaufmann@unibas.ch, Assessor) |
| Content | 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. |
| Learning objectives | - 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. |
| Bibliography | 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. |
| Comments | The course will be taught "in class". |
| Weblink | Weblink |
| Admission requirements | 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) |
| Course application | 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! |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| 14-täglich | Wednesday | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Comments | The course will be taught "in class". |
| Date | Time | Room |
|---|---|---|
| Wednesday 23.02.2022 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 09.03.2022 | 14.15-18.00 | Fasnachtsferien |
| Wednesday 23.03.2022 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 06.04.2022 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 20.04.2022 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 04.05.2022 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 18.05.2022 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 01.06.2022 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Modules |
Modul: Fachlich-methodische Weiterbildung (Doctoral Studies - Faculty of Business and Economics) Module: Field Electives in Economics and Public Policy (Master's Studies: Economics and Public Policy) Module: Specific Electives in Data Science and Computational Economics (Master's Studies: Business and Economics) Module: Statistics and Computational Science (Master's Studies: Actuarial Science) Specialization Module: Areas of Specialization in International and/or Monetary Economics (Master's Studies: International and Monetary Economics) Specialization Module: Quantitative Methods (Master's Studies: Business and Economics (Start of studies before 01.08.2021)) |
| Assessment format | record of achievement |
| Assessment details | 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. https://wwz.unibas.ch/de/studium/pruefungen/vorlesungs-und-pruefungsraeume/ 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. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,1 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |