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2026/2027  BA-BHAAV1016U  Quantitative Methods

English Title
Quantitative Methods

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for General Management
Programme Bachelor of Science in Economics and Business Administration
Course coordinator
  • Morten Lau - Department of Economics (ECON)
Main academic disciplines
  • Finance
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Blended learning
Last updated on 30-01-2026

Relevant links
Learning objectives
The course will provide students with a practical understanding of basic statistical models and methods in data analysis. To obtain a top grade, students are required to have a good understanding of the main concepts and models in applied econometrics that are covered in the course. This includes the ability to:
  • Demonstrate knowledge of concepts, models, methods, and tools in applied econometrics.
  • Evaluate empirical studies by others.
  • Implement econometric analyses in practice.
Course prerequisites
Basic knowledge of descriptive statistics and probability distributions.
Examination
Quantitative Methods:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Aids Limited aids, see the list below:
The student is allowed to bring
  • USB key for uploading of notes, books and compendiums in a non-executable format (no applications, application fragments, IT tools etc.)
  • Any calculator
  • In Paper format: Books (including translation dictionaries), compendiums and notes
The student will have access to
  • Canvas
  • Basic IT application package
  • The personal drive (S-drive) on CBS´ network
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Description of the exam procedure

The written exam is preceded by a 48-hour preparatory period where students are undertaking data analysis. Results from the data analysis are subsequently used in the 4-hour written exam.
Course content, structure and pedagogical approach

This course provides a rigorous introduction to econometric methods used to analyze economic and behavioral data. In contrast to first-year statistics, which focuses on foundational concepts and probability tools, this course emphasizes empirical model building, estimation, interpretation, and causal reasoning in applied research settings.

 

The course begins with an introduction to empirical analysis and progresses through increasingly advanced econometric models. Students learn the simple and multiple linear regression model; nonlinear and interaction specifications; binary and multinomial outcome models; panel-data methods including fixed and random effects; instrumental variables and the logic of identification; and maximum likelihood estimation. These topics mirror the structure of the lecture plan and are reinforced in hands-on exercise classes working with real-world datasets across a variety of social-science contexts.

 

Throughout the course, students develop the ability to translate theoretical or institutional questions into testable empirical models, evaluate the assumptions behind different estimators, and interpret results in a conceptually sound way. While the course is broadly applicable to economics, management, public policy, and related fields, occasional examples connect to behavioral finance, illustrating how econometric tools can be used to study systematic patterns in financial decision making.

 

An integral component of the course is practical data analysis in Stata. Students will learn to implement the methods covered in the lectures, diagnose model limitations, conduct robustness checks, and critically assess empirical claims in academic and applied research. By the end of the course, students will be prepared to execute independent empirical projects and engage with modern econometric evidence at a level well beyond introductory statistics. 
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
Description of the teaching methods
The course combines lectures with practical exercise classes to develop both conceptual understanding and hands-on empirical skills. Lectures introduce the econometric methods, formal intuition, and underlying assumptions, with an emphasis on how these tools are applied in empirical research. Throughout the lectures, illustrative examples and short demonstrations are used to link theoretical material to applied questions in economics and behavioral research.

Exercise classes provide students with the opportunity to implement the methods presented in the lectures using Stata. Working with real-world datasets, students complete guided empirical tasks, run estimations, diagnose model assumptions, and interpret results. These sessions are designed to deepen understanding through practice, reinforce statistical reasoning, and build proficiency in empirical research workflows.

The teaching approach is interactive and application oriented. Students are expected to engage actively with both the theoretical and empirical material through problem-solving, software-based exercises, and critical discussion of empirical evidence. By integrating conceptual lectures with structured hands-on analysis, the course equips students with the practical skills required to perform independent econometric work and to critically assess empirical findings in academic and applied contexts.
Feedback during the teaching period
The course offers continuous feedback to students during lectures and exercise classes as well as office hours. Feedback includes discussions of topics and empirical examples in class, and lectures and exercise classes provide plenty of opportunities for questions and discussions. Continuous feedback is a key component of teaching and learning activities in the course.
Student workload
Preparation / exam 170 hours
Lectures 24 hours
Exercises 24 hours
Expected literature

Stock, J. H. and Watson, M., Introduction to Econometrics, Global Edition, 4th edition (Pearson Education, 2019); and academic papers relevant to the various topics in the course.
Last updated on 30-01-2026