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2025/2026  BA-BHAAI1108U  Introduction to Econometrics with R

English Title
Introduction to Econometrics with R

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration Summer
Start time of the course Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 30
Max. participants 100
Study board
Study Board for General Management
Programme Bachelor of Science in Economics and Business Administration
Course coordinator
  • Marta Boczon - Department of Economics (ECON)
Main academic disciplines
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 20/11/2025

Relevant links
Learning objectives
  • Unlock R’s hidden tricks: Use R's help documentation to figure out exactly what functions do, which formulas they rely on, and the methods working under the hood.
  • Take control of R commands: Change R's default settings, build your own functions, and applying them to real data.
  • Harness the power of matrices: Carry out matrix operations and use them in R to make your code faster and smarter.
  • Be the decision-maker: Pick the most suitable estimation method, distribution, or type of analysis for real-world problems.
  • Bridge theory and practice: Derive maximum likelihood (ML) and method of moments (MM) estimators on paper, then code them in R.
  • Test estimator quality: Work out their properties (bias, consistency, efficiency) and use R to explore them numerically.
  • Bring distributions to life: Derive their moments, visualize them in R, simulate random draws, calculate exact probabilities, and use them for statistical testing.
  • Tell the bigger story: Separate “just describing data” from actually drawing conclusions, knowing when theory lets us generalize and when the mean is just… the mean.
Course prerequisites
Knowledge of the statistical language R is not required.
Examination
Introduction to Econometrics with R:
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 Summer
Aids Limited aids, see the list below:
The student is allowed to bring
  • An approved calculator. Only the models HP10bll+ or Texas BA ll Plus are allowed (both models are non-programmable, financial calculators).
  • Language dictionaries in paper format
The student will have access to
  • Advanced IT application package
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.
n/a
Description of the exam procedure

The exam covers the entire course content.

All  learning objectives are relevant for this exam.
Course content, structure and pedagogical approach

Quantitative analysis is increasingly used for problem solving in economics, business, and finance. However, a skillful analysis requires profound knowledge of the underlying statistical methods and statistical programming skills.

 

The objective of this course is to introduce you to fundamental concepts of econometrics and data analysis that form the basis for data driven decision making, empirical analysis of causal relationships, and forecasting, and to sharpen your technical skills for problem solving at workplace and in other real-life settings.

 

The concepts that you will learn in this course will equip you with skills and knowledge necessary to excel in more advanced econometrics and applied statistics courses at CBS (e.g., BA-BMECV1031U Econometrics, KAN-COECO1058U Econometrics, KAN-COECO1056U Financial Econometrics, KAN-CMECV1249U Panel Econometrics) and elsewhere.

 

The course will also introduce you to programming with R, the main programming language for statistical computing. We will begin with basic R operations and gradually progress to writing our own functions.

 

Throughout the semester, we will work with real data on 911 calls to the New York City Police Department. In each class, we will analyze a different aspect of the dataset, allowing us to become familiar with its structure, identify data anomalies, learn how to address them, and use econometric tools to extract important insights, assess credibility, avoid over- or under-selling results, and draw meaningful conclusions. By the end of the course, you will be well on your way to becoming a confident statistical programmer, with the ability to apply the tools you learn to any dataset of your choice.
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
  • Methodology
  • Models
Research-like activities
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
Description of the teaching methods
A word from the lecturer:

“I will briefly introduce new concepts and demonstrate each one with examples, after which we will apply the concepts to learn something new from our data.

Each lecture will be hands-on, and I hope you will ask questions and actively participate.

At least half of every class will be devoted to demonstrating and practicing the course material in R. The remaining time will be spent solving problems individually, in groups, or on the whiteboard.

After each class, you will receive voluntary homework assignments. These will not be graded, but if you choose to submit them, I will provide individual feedback. The homework will follow the same structure as the final exam, making it excellent preparation. Moreover, towards the end of the semester I will provide you with a mock final exam for practice.

The course consists of 38 hours of lectures, exercises, and lab sessions.

My teaching approach is lecture-based but highly interactive.

In particular, I will:
1. Keep lessons brief and focused.
2. Allocate time for questions.
3. Use visual cues to support learning.
4. Explain new concepts with clear examples.
5. Provide solutions to homework and in-class exercises.
6. Encourage active class participation.
7. Promote collaborative problem-solving and teamwork.

Hope to see many of you there!”
Feedback during the teaching period
• Weekly on-campus office hours.
• Virtual office hours by appointment.
• Email correspondence.
• Feedback on homework.
Student workload
Lectures 38 hours
Exam 4 hours
Preparation 164 hours
Further Information


6-week course.

 

Precourse activity:   

 

At the end of May, the course coordinator will upload the precourse activity on Canvas. Students are expected to complete it, as it will be included in the final exam. However, the assignment will not be graded independently.

 

The precouse activity will consist of:

A math and statistics refresher
DataCamp introduction to R programming
Expected literature

Supplementary literature: 

 

Statustics : A Modern Introduction to Probability and Statistics Understanding Why and How by F.M. Dekking, C. Kraaikamp, H.P. Lopuhaae, and L.E. Meester

 

Programing in R : R Programming for Beginners: An Introduction to Learn R Programming with Tutorials and Hands-On Examples ny N. Metzler

 

Calculus:  A Complete Introduction - The Easy Way to Learn Algebra by Hugh Neill

 

Matrices : Elementary Linear Algebra (10th ed.) by H. Anton and C. Rorres
Last updated on 20/11/2025