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2021/2022  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 MSc in Economics and Business Administration
Course coordinator
  • Lisbeth La Cour - Department of Economics (ECON)
Main academic disciplines
  • Finance
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Blended learning
Last updated on 15-02-2021

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 and sources
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 Individual exam
Size of written product Max. 15 pages
Assignment type Project
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

Students conduct a short empirical project with time series data of their choice. In this project they apply the econometric methods which have been covered in the lectures. Besides conducting the empirical analysis, students are to show an understanding of the methods, justifiy their choice of the econometric models and interpret the results appropriartely. The project is written in parallel with the course and is of 15 A4-pages. The project must be submitted at the end of the teaching term.
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:

 

- Finding and working with data

- Time Series prediction (using ARIMA and Autoregressive Distributed Lag models/dynamic regression models)

- ARCH / GARCH modelling

- Times Series Econometric and Machine Learning Method. The latter topic is only briefly discussed.

- Multivariate time series model (VAR)

- Cointegration

- Hands-on experience with a selected (by course instructor) statistical software package
Description of the teaching methods
The course is composed as a mix of lectures and computer based hands-on exercises
Feedback during the teaching period
Feedback is available on a continuous basis thoughout the semester. Lectures are structured so that they consists of both teacher presentations and voluntary exercises done by the students before class. Feed-back for each of these elements are as follows: During teacher presentations 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. Pre-solved exercises are discussed and/or solutions handed out and individual feed-back can then be obtain during office hours or in the breaks. The students are very welcome during the office hours of the teacher, as well as online any time. Feedback can be provided to student regarding their choice of data for the exam project, on technical issues in relation to the use of R in general (only to a limited degree for their exam project as no supervision is allowed. Minor questions can be answered if the teacher does not find it unfair to the other students), as well as questions which 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 15-02-2021