# GRA 6039 Econometrics with Programming

## GRA 6039 Econometrics with Programming

Course code:
GRA 6039
Department:
Economics
Credits:
6
Course coordinator:
Vasilis Sarafidis
Course name in Norwegian:
Econometrics with Programming
Product category:
Master
Portfolio:
MSc - Core course
Semester:
2021 Autumn
Active status:
Active
Level of study:
Master
Teaching language:
English
Course type:
One semester
Introduction

The aim of the course is to equip the students with an understanding of econometric techniques at a level expected among master students in economics, finance and related disciplines. Programming will be introduced and used as a natural part of data analysis, and simulation will be used to assess the finite sample behaviour of large sample techniques, and to assess robustness properties of statistical methods. Both theoretical and practical exercises will be given.

Learning outcomes - Knowledge

After taking this course, students should have a solid knowledge of linear regression models and estimation theory under econometric assumptions, as well as gaining practical experience in applying these models using modern software.

Students should also be able to independently write programs for data analysis, perform simulation experiments, and develop their critical reasoning for econometric investigations.

Learning outcomes - Skills

Econometrics: Using and motivating the use of linear regression models.

Programming: Instructions in programming will be given. This includes control-structures, such as if-statements and loops, data importation and reorganization, the use of visualization techniques, programming as well as using descriptive statistics, calling upon and implementing statistical procedures, as well as writing simulation experiments.

General Competence

Through experience with econometric models and computer experiments, the student will reflect on the limitations of econometrics, the issue of subjectivity in reaching statistical conclusions, and the level of trust one may place in statistically based decisions. Further, simulation will be introduced as a tool to assess the validity of econometric techniques. The student will reflect on using large-sample techniques in finite samples, the assessment of econometric assumptions and the concept of robustness in econometrics.

Course content
1. Review of probability and basic statistics.
2. Statistical inference.
3. Multiple linear regression.
Teaching and learning activities

-

Software tools
R/R-Studio

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

All parts of the assessment must be passed in order to receive a final grade in the course.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

### Covid-19

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

### Teaching

Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Assessments
Assessments
Exam category:
Submission
Form of assessment:
Written submission
Weight:
40
Grouping:
Group (1 - 3)
Duration:
1 Week(s)
Comment:
Assignment
An oral defense of the assignment might be required.
Exam code:
GRA60393
ECTS
Resit:
Examination when next scheduled course
Exam category:
Submission
Form of assessment:
Written submission
Invigilation
Weight:
60
Grouping:
Individual
Support materials:
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Duration:
3 Hour(s)
Comment:
Final written examination
Exam code:
GRA60394
ECTS
Resit:
Examination when next scheduled course
Type of Assessment:
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight:
100
ActivityDurationComment
Teaching
36 Hour(s)
Lectures
Teaching
6 Hour(s)
Practical Matlab-work under supervision.
Examination
15 Hour(s)
Home-exam related work.
Examination
3 Hour(s)
Final exam
Student's own work with learning resources
100 Hour(s)