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Time Series Econometrics for Finance

The overall aim of the course is to equip students with a solid knowledge in Time Series Econometrics which will be relevant for the empirical part of the students’ Master’s thesis projects.

The course provides students with an in-depth knowledge of some fundamental concepts in time series econometrics and outlines a few common difficulties that arise when dealing with time series data in theory as well as in practice. This course is a foundation course in Time Series Econometrics. Time series data are commonly encountered in finance, as well as in marketing and sales, where observations are recorded over time.

Since time series data are different in terms of their nature to cross sectional data studied in foundation econometrics, Time Series Econometrics has become a specialized field of study over the last 60 or so years, and as such requires a different set of tools for analysis.

The course is designed to bring students that have not had any exposure to time series data and/or analysis up to speed and to offer a more detailed treatment to students that are already familiar with some time series concepts.

The course covers the following foundation topics in time series: Autoregressive
Moving Average models (ARMA), non-stationarity, unit-roots and decompositions of time series, Vector Autoregressions (VARs), Cointegration and Vector Error Correction models (VECMs), as well as estimation and forecasting with such models.

  • Course structure

    The teaching consists of lectures that incorporate analytical as well as computer exercises, and requires a significant portion of self-study on the part of students.
    The course workload is 200 hours, equivalent to 7,5 higher education credits.
    The language of instruction is English.


    Teaching format

    Teaching takes place on campus, unless otherwise specified by the course director.


    Assessment for the course will be continuous and is carried throughout the different course activities. Each assessment task is weighted in relation to its importance in the overall assessment of the course. The student’s results from the different assessment tasks are added up to a total course score that will then translate into the final grade for the course.

    Assessment tasks
    The course contains the following weighted assessment tasks:
    1. Individual written examination.
    2. Two individual assignments consisting of analytical calculations and computer-based exercises

    The examination is conducted in English.



  • Schedule

    The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
  • Contact

    Course coordinator:

    Head of course: Daniel Buncic