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: 
2023 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. Linear Regression
  2. Statistical inference
  3. Instrumental Variables Estimation and Two Stage Least Squares
  4. Panel Data Analysis
  5. Limited Dependent Variables
Teaching and learning activities

-

Software tools
R/R-Studio
Stata
Additional information

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 specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

 

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 60393
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
40No1 Week(s)Group (3 - 4)Assignment. An oral defense of the assignment might be required.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 60394
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
60Yes3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Final written examination
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Group (3-4)
Support materials:
Duration:1 Week(s)
Comment:Assignment. An oral defense of the assignment might be required.
Exam code:GRA 60393
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:60
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:3 Hour(s)
Comment:Final written examination
Exam code:GRA 60394
Grading scale: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
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Lectures and Tutorials
Examination
15 Hour(s)
Home-exam related work
Examination
3 Hour(s)
Final exam
Student's own work with learning resources
100 Hour(s)
Sum workload: 
154

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.