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2019/2020  KAN-CDASO2040U  Predictive Analytics

English Title
Predictive Analytics

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory offered as elective
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Time series econometrics
    Lisbeth La Cour - Department of Economics (ECON)
The teacher need not be the course responsible person.
Main academic disciplines
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Blended learning
Last updated on 03-07-2019

Relevant links

Learning objectives
To achieve the grade of 12, students should meet the following learning objectives only with no or minor mistakes or errors. By the end of the course the students will be able to:
  • Explain the concepts, models and methods introduced during the course.
  • Identify and perform an academically founded forecasting analysis in practice (including taking relevant theoretical relationships into account) using the tools introduced during the course.
  • Discuss and solve any important problems encountered in relation to the analysis.
  • Evaluate the forecasts of the analysis.
  • Report/communicate the results and conclusion of the analysis both if the reader is a statistician/econometrician and if the reader is e.g. a CEO.
  • Evaluate a forecasting analysis conducted by another person/researcher.
  • Use a statistical analysis software package for the analysis (e.g. R or SAS) and demonstrate that you can interpret and evaluate the output from the software package.
Course prerequisites
The course assumes familiarity with either a basic courses in statistics and regression analysis or a basic course in econometrics. Concepts such as random variables and familiarity with the principles of statistical hypotheses testing are assumed known.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved: 2
Compulsory home assignments
Each assignment is 1-3 pages in group of 1-4 students.
The students have to get 2 out of 4 assignments approved in order to go to the exam.

There will not be any extra attempts provided to the students before the ordinary exam.
If a student cannot hand in due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (max. 10 pages) which will cover 2 mandatory assignments.
Predictive Analytics:
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 Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Students can submit a revised project.
Course content, structure and pedagogical approach

Predictive analytics is a field that potentially can provide managers with very valuable tools for decision making. Both for sales variables of firms but also for financial variables like stock prices forecasting models and methods may be of interest, and during the course we will discuss challenges in relation to preparing relevant data for forecasting, choosing well-suited models for analysis and we offer a range of tools for evaluating the forecasting performance of the models we present. In one of the lectures we introduce a tool that can be very helpful for model selection if ‘Big  Data’ is available.

This course is designed to provide both a theoretical foundation for predictive modeling but also hand-on experience with analyzing economic data. The models presented in the course will belong to different fields of statistics but in all cases the predictive power of the models will be in focus. Introducing statistical tools allows us to not only come up with an actual forecast of an economic variable of interest (think e.g. of sales) but also to assess the uncertainty of the forecast. Before a proper model can be selected it is of interest to discuss issues about data availability and also data reliability. Such topics are often not very much discussed in the text book but providing the students with examples of the types of challenges one may encounter – based on the research of the teachers – will try to fill this gap. 

Description of the teaching methods
Lectures, in-class exercises during lectures, exercise classes with tools training.
Feedback during the teaching period
On a continuous basis in class. This holds true for both lectures and exercise classes. In addition the students are very welcome during the office hours of the teacher. During office hours students can receive feed-back both on 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). Also feed-back on theoretical questions is provided during office hours. Finally individual feed-back to students are provided during the assessment of the mandatory home assignments and indicative solutions will also be provided.
Student workload
Class lectures 24 hours
Exercise classes 24 hours
Class preparation incl. home assignments 101 hours
Exam and exam preparation 76 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy the books.


Main text book(s) and articles:

Hyndman, R.J & Athana­sopou­los, G (2014): Forecasting: principles and Practice   (HA)


Gujarati, D.N. and Porter, D.C. (2010). Essentials of Econometrics, 4th edition, McGraw-Hill, pp 201-205 and 386-397


Doornik, J.A. & Hendry,  D.F.  (2014): Statistical model selection with ‘Big Data’. University of Oxford, Department of Economics, Discussion Paper Series,  # 735  (or more recent text in this spirit). (DH)


More supplementary stuff:

Buus Lassen, N., la Cour, L., Vatrapu, R. (2017), ‘Predictive Analytics with Social Media data’ in Sloan & Quan-Haase ed. The SAGE Handbook of Social Media Research Methods, Chapter 20, pp 328-341 (BCV)


Pretis, F., Reade J.J. and Sucarrat G. (2018). Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks, Journal of Statistical Softeware, vol 86, 3, pp 1-44


Last updated on 03-07-2019