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2024/2025  KAN-CCMVV1727U  Time Series for Economics, Business and Finance

English Title
Time Series for Economics, Business and Finance

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 150
Study board
Study Board for cand.merc. and GMA (CM)
Course coordinator
  • Marta Boczon - Department of Economics (ECON)
Main academic disciplines
  • Finance
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Blended learning
Last updated on 04-02-2024

Relevant links

Learning objectives
  • Understand the theoretical foundations and assumptions behind time series models.
  • Interpret and communicate the results obtained from time series models, including parameter estimates, significance tests, and forecast intervals (see Nordic Nine #6: You are critical when thinking and constructive when collaborating).
  • Clearly articulate the implications of the analysis for decision-making in business, economics, or finance.
  • Demonstrate the ability to preprocess time series data by handling missing values, outliers, and irregularities
  • Apply techniques for data cleaning and normalization to ensure the quality of the dataset
  • Utilize R programming language for various tasks in time series analysis, including data manipulation, model fitting, and visualization (see Nordic Nine #2: You are analytical with data and curious about ambiguity).
  • Work on practical projects that involve applying time series analysis techniques to real-world economic, business, or financial datasets (see Nordic Nine #4: You are competitive in business and compassionate in society).
  • Identify common challenges in time series analysis, such as seasonality, non-stationarity, and autocorrelation
  • Discuss techniques for addressing these challenges, including differencing, detrending, and model selection. (see Nordic Nice #3: You recognize humanity’s challenges and have the entrepreneurial knowledge to help resolve them)
  • Generate informative visualizations, such as time plots, seasonal decomposition plots, and autocorrelation plots
  • Interpret visualizations to extract meaningful patterns and trends in the time series data.
Course prerequisites
Introduction to statistics and basic econometrics (with regression).
Time Series for Economics, Business and Finance:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 2-4
Size of written product Max. 10 pages
Definition of number of pages:
Groups of
2 students 5 pages max.
3-4 students 10 pages max.
Assignment type Synopsis
Release of assignment Subject chosen by students themselves, see guidelines if any
Written product to be submitted on specified date and time.
10 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Re-take exam is to be based on the same report as the ordinary exam:

*if a student is absent from the oral exam due to documented illness but has handed in the written group product she/he does not have to submit a new product for the re-take.

*if a whole group fails the oral exam they must hand in a revised product for the re-take.

*if one student in the group fails the oral exam the course coordinator chooses whether the student willhave the oral exam on the basis of the same product or if he/she has to hand in a revised product for there- take.
Description of the exam procedure

Working in groups of 2-4 you are supposed to conduct a short empirical project with time series data of your own choice. In summary, you need to (i) find the data, (ii) apply and evaluate at least two different models for the data from the course's syllabus, (iii) demonstrate understanding of the methods you chose to apply, (iv) justify the choices you have made regarding the data and the methods, (v) interpret the results appropriately (see Nordic Nine #6: You are critical when thinking and constructive when collaborating).


The theme(s) of the synopsis must be prepared by the student(s). The oral examination will be based on the synopsis. The examiner may ask questions within the framework of the entire syllabus.


The synopsis is written in parallel with the elective. The synopsis must be submitted two weeks before the date of the exam.

Course content, structure and pedagogical approach

Upon successfully finishing this course, students will acquire the expertise to meticulously clean, visually represent, and analyze time series data crucial in the realms of business, economics, and finance. This journey of learning encompasses mastering various data collection methods, including adeptly extracting information from online sources. Emphasizing the widely utilized R software, prevalent in financial institutions, firms, and academia, students will become adept at data manipulation—empowering them with a coveted skill set. 


The curriculum delves into time series methodologies, enabling students to construct models and make insightful forecasts. Armed with these skills, students will find themselves well-equipped for roles in analytics departments across industries or poised for advanced academic pursuits in fields such as economics, finance, marketing, and related disciplines (see Nordic Nice #3: You recognize humanity’s challenges and have the entrepreneurial knowledge to help resolve them). This course serves as a springboard, propelling students toward a future where they can harness the power of data to drive informed decision-making in diverse professional settings (see Nordic Nine #4: You are competitive in business and compassionate in society).



  • Autoregressive Integrated Moving Average  (ARIMA) model.
  • Seasonal Autoregressive Integrated Moving Average  (SARIMA) model.
  • Modelling structural breaks.
  • Autoregressive Distributed Lag (ADL) model.
  • Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model.
  • Vector Autoregressive (VAR) model.
  • Structural Vector Autoregressive (SVAR) model.
  • Error Correction Model (ECM).
Description of the teaching methods
The course is a mix of lectures and computer-based, hands-on examples of how to use R for time series data analysis
Feedback during the teaching period
Feedback is available on a continuous basis throughout the semester.
Student workload
Classes 30 hours
Exam / preparation 176 hours
Expected literature

Applied Econometric Time Series - Walter Enders, 4th Edition, Wiley



Last updated on 04-02-2024