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| Semester | Herbstsemester 2017 |
| Angebotsmuster | Jedes Herbstsemester |
| Dozierende | Christian Kleiber (christian.kleiber@unibas.ch, BeurteilerIn) |
| Inhalt | Contents: The course covers basic concepts of univariate time series analysis that are needed for an understanding of modern time series econometrics. The latter refers to topics such as unit roots, volatility models, vector autoregressions (VARs), and cointegration, which are studied in the follow-up course Time Series Analysis II in the second half of the semester. Time Series Analysis I will study notions such as autocorrelation, stationarity, AR(I)MA models and their seasonal extensions, model selection and diagnostics, and forecasting. Empirical illustrations will make use of the R language for statistical computing and graphics, see https://www.R-project.org/, hence basic knowledge of this software package is expected. |
| Lernziele | Basics of univariate time series analysis. |
| Literatur | Some references (the course will not necessarily follow a specific text!): Brockwell PJ, Davis RA (2016). Introduction to Time Series and Forecasting, 3rd ed. Springer. [Available in electronic form via the university library.] Chan NH (2010). Time Series: Applications to Finance with R and S-Plus, 2nd ed. Wiley. Cryer JD, Chan KS (2008). Time Series Analysis, with Examples in R, 2nd ed. Springer. [Available in electronic form via the university library.] Franses PH, van Dijk D, Opschoor A (2014). Time Series Models for Business and Economic Forecasting, 2nd ed. Oxford Univ. Press. Ruppert D, Matteson DS (2015). Statistics and Data Analysis for Financial Engineering, 2nd ed. New York: Springer. [Available in electronic form via the university library.] Tsay RS (2010). Analysis of Financial Time Series, 3rd ed. Wiley. |
| Weblink | Weblink |
| Teilnahmebedingungen | WWZ students: BA completed. In addition, successful completion of Econometrics (MSc) is strongly recommended. Some basic knowledge of R is expected. Other participants: Basic statistics (incl. regression basics) and mathematics. |
| Anmeldung zur Lehrveranstaltung | Course registration: please enrol in MOnA; Eucor students enrol at the Students Services at Petersplatz 1 within the registration deadline. Registration = Admission to the exam |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
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Keine Einzeltermine verfügbar, bitte informieren Sie sich direkt bei den Dozierenden.
| Module |
Modul Statistik und Computational Science (Masterstudium: Actuarial Science) Spezialisierungsmodul: Areas of Specialization in International and/or Monetary Economics (Master International and Monetary Economics) Vertiefungsmodul Monetary Economics and Financial Markets (Master Wirtschaftswissenschaften) Vertiefungsmodul Quantitative Methods (Master Wirtschaftswissenschaften) |
| Leistungsüberprüfung | Semesterendprüfung |
| Hinweise zur Leistungsüberprüfung | Written exam. Students may bring (1) a dictionary, (2) a calculator (subject to the usual rules), and (3) two pages of their own, handwritten notes. In addition, there will be several assignments, for which students may work in groups of two. Assignments will account for 20% of the final grade. written exam: 20.11.17; 08:15 -10:00. WWZ Auditorium: A-Z. You can still withdraw from the examination by submitting a completed, signed form to our office from 17.10.17 until 27.10.17 / 12:00 o’clock. Withdrawals sent by email will not be accepted. You will find the examination withdrawal form on the Homepage of the Student Dean’s Office. Prior to 16.10.17, please only use MONA for withdrawing. The exam rooms will be published up to 08.12.17. |
| An-/Abmeldung zur Leistungsüberprüfung | Anmeldung: Belegen |
| 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 |