English   Danish

2024/2025  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 BSc 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 07/11/2024

Relevant links
Learning objectives
  • Analyze and compare different methods of estimation
  • Carry out basic operations on matrices
  • Carry out data estimation.
  • Compute risk and average return on an investment.
  • Determine the distribution which best suits the data
  • Determine the most and least likely event
  • Modify standard R commands.
  • Read and make use of R help
  • State properties of distributions and estimation methods.
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 and 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.
1st retake is a 4-hour written sit-in exam.
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 of high dimensional data sets 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. In particular, 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. Finally, this course will sharpen your technical skills for problem solving at workplace and in other real-life settings.

Throughout the course, we will learn about matrices and their use in linear regression analysis, probability distributions and their role in carrying out valid data approximations, and estimation methods and their importance in producing credible results of any data analysis.

The course will also introduce you to programming with R, which is the  main programming language of statistical computing. We will start out with basic R operations and then, with time, we will learn about ways to write our own functions in R. In this way, you will be set on a path of becoming a statistical programmer
Description of the teaching methods
A word from the lecturer: “I will demonstrate each new concept with examples, after which we will solve together a number of exercises. This way, each lecture will be hands-on, where I hope you will ask many questions and actively participate in the class. During each class, we will spend up to one hour demonstrating and practicing the course material in R. After each class you will be assigned voluntary homework. The homework will not be graded however if submitted it will be returned with individual feedback.
The course comprises of 38 hours of lectures, exercises, and lab sessions. The course is based on lecture-based learning where I will (1) keep all lessons brief, (2) allocate time for questions, (3) use visual cues to facilitate learning, (4) explain new concepts with examples, (5) provide solutions to homework and in-class exercises, (6) encourage effective class participation, (7) promote collaborate problem solving and teamwork.”
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.

 

Preliminary Assignment:   

The course coordinator uploads Preliminary Assignment on Canvas at the end of May. It is expected that students participate as it will be included in the final exam, but the assignment is without independent assessment and grading.

 

We will discuss and review it during the first lecture.

 

 
Expected literature

A Modern Introduction to Probability and Statistics Understanding Why and How  F.M. Dekking C. Kraaikamp H.P. Lopuhaa¨ L.E. Meester

 

R Programming for Beginners: An Introduction to Learn R Programming with Tutorials and Hands-On Examples Nathan Metzler

 

Algebra: A Complete Introduction - The Easy Way to Learn Algebra Hugh Neill
Last updated on 07/11/2024