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2023/2024  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 100
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 15-02-2023

Relevant links
Learning objectives
  • Explain different time series models from econometrics and those discussed in class for machine learning. Explain the applications of the models.
  • Predict time series relevant for business, economics, and finance
  • Explain output/results from time series analysis and predictions
  • Correctly download, clean, and organize time series data from a variety of online databases.
  • Use R to perform an independent data analysis for the final project
Course prerequisites
Introduction to statistics and basic econometrics (with regression).
Examination
Time Series for Economics, Business and Finance:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Group exam
Please note the rules in the Programme Regulations about identification of individual contributions.
Number of people in the group 2
Size of written product Max. 25 pages
The project is of maximum 25 A4-pages excluding the list of references. Submission of your R script is required. Moreover, you can attach an appendix of a maximum of 15 A4-pages.

If you opt to write your project individually it cannot exceed 15 A4-pages. The project must be submitted at the end of the teaching term.
Assignment type Project
Release of assignment Subject chosen by students themselves, see guidelines if any
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

Working in pairs 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. .
Course content, structure and pedagogical approach

Upon completion of the course, students will be able to clean, visualize, and analyze time series data in business, economics, and finance. Students will learn methods of data collection, including obtaining data from online data sources; data manipulation with software widely used at financial institutions, firms and in academia; time series and machine learning methods for model building; as well as forecasting and prediction. With the skills taught in the course, students will be well prepared for analytics departments in industry or further academic studies in economics, finance, marketing, and related disciplines. 

 

Topics: 
- Autoregressive Integrated Moving Average  (ARIMA) model.
- Autoregressive Distributed Lag (ADL) model.
- Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model.
- Vector Autoregressive (VAR) model.
- Error Correction Model (ECM).
- A brief overview of machine learning methods for time series econometrics
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. During lectures, students are encouraged to ask questions and such questions will be discussed. In addition, teacher and student frequently discuss relevant exam and news topics in class. The students are very welcome during the office hours of the teacher, as well as online any time during the teaching period. Regarding your final project, we offer feedback in relation to your choice of topic, data and methods. Moreover, we are able to assist you with questions that pertain to the structure and theory included in the exam paper.
Student workload
Classes 33 hours
Exam / preparation 173 hours
Expected literature

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

 

Further recommended readings and journal articles will be posted on Learn.
Last updated on 15-02-2023