GRA 6039 Econometrics with Programming

GRA 6039 Econometrics with Programming

Course code: 
GRA 6039
Department: 
Economics
Credits: 
6
Course coordinator: 
Steffen Grønneberg
Course name in Norwegian: 
Econometrics with Programming
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2017 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 behavior 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 the general linear regression model, its most common extensions – including time series analysis – 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 Matlab programmes related 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 and autoregression-based time-series models.

Programming: Instructions in general Matlab-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.

Learning Outcome - Reflection

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 techniques will be introduced in order to assess the validity of an econometric technique. 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. Multiple linear regression.
  3. Time series models.
Learning process and requirements to students

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 that is not included on itslearning or text book.

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

Software tools
Matlab
Additional information

RESIT EXAMINATION
Students who have taken the 2016/2017 version of this course, may resit by attending the exam in the 2017/2018 version.  

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.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA60393
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
40No2 Week(s)Group ( 2 - 3)Assignment An oral defense of the assignment might be required.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA60394
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
60Yes3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Final written examination with supervision
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Group (2-3)
Support materials:
Duration:2 Week(s)
Comment:Assignment An oral defense of the assignment might be required.
Exam code:GRA60393
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 with supervision
Exam code:GRA60394
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam organisation: 
Ordinary examination
Total weight: 
100
Sum workload: 
0

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.