GRA 6648 Research Methodology - Economics

GRA 6648 Research Methodology - Economics

Course code: 
GRA 6648
Department: 
Economics
Credits: 
6
Course coordinator: 
Hilde Christiane Bjørnland
Leif Anders Thorsrud
Course name in Norwegian: 
Research Methodology - Economics
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2021 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

This course is a graduate level introduction to Econometrics.

Learning outcomes - Knowledge

Students will learn to use statistical methods for estimating economic relationships, testing economic theories, and using estimated models to analyze the effect of policy intervention for the public and the private sector. The focus will be on time series econometrics.

Learning outcomes - Skills

The students will learn about the fundamentals of time series modelling, and how to use panel data in a time series context. Applications in macroeconomics and finance, using both traditional economic data and Big Data sources, will be covered.  The students will master univariate and multivariate models of stationary and non stationary time series, and learn methods for conducting structural (counterfactual) inference when working with this type of data. In the end, they should be able to design and execute applied econometric projects.

The students will also obtain an understanding of information search strategies. Including:

  • Acquaintance with methods for information, harvesting and search techniques
  • Know what a critical literature review is and how this type of articles may be searched for and used
  • Critical evaluation of sources
General Competence

Students should be able to reflect and understand the basic techniques used in applied econometrics, so that eventually they can master and produce sophisticated applied econometric analysis.

Course content

I Data Management

  • Traditional data (time series and panel data)
  • Big data
  • Data reduction
  • Web scraping

II Time series – Stationary and non-stationary univariate time series

  • White noise, moving average, autoregressive models
  • Forecasting
  • Deterministic and stochastic trends, unit roots, structural change
  • Applications

III Vector autoregression (VAR) methodology

  • Structural VARs specification and estimation
  • Identification, impulse responses, variance decomposition
  • Spurious regression and log run economic relationships
  • Applications

IV Panel data analysis

  • Difference in difference methodology
  • Fixed effects
  • Applications

V Introduction to information search strategies (3 hours in the pc-lab)

  • Acquaintance with methods for information, harvesting and search techniques
  • Know what a critical literature review is and how this type of articles may be searched for and used
  • Critical evaluation of sources

VI Setting up an econometric project

  • Research ethics, data handling, specification, modeling, policy analysis
Teaching and learning activities

Lectures and practical exercises that must be solved on the computer.

Software tools
Matlab
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.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.

At resit, all exam components must, as a main rule, be retaken during next scheduled 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.

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.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Activity
Form of assessment:
Presentation
Exam code:
GRA66486
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No -Group (3 - 6) Group assignment and presentation
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA66486
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
10No 1 Week(s)Group/Individual ( 1 - 3)Library assignment
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA66486
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
70Yes3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Final written examination under supervision.
Exams:
Exam category:Activity
Form of assessment:Presentation
Weight:20
Invigilation:No
Grouping (size):Group (3-6)
Support materials:
Duration: -
Comment: Group assignment and presentation
Exam code:GRA66486
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:10
Invigilation:No
Grouping (size):Group/Individual (1-3)
Support materials:
Duration: 1 Week(s)
Comment:Library assignment
Exam code:GRA66486
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:70
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:3 Hour(s)
Comment:Final written examination under supervision.
Exam code:GRA66486
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100
Student workload
ActivityDurationComment
Teaching on Campus
36 Hour(s)
Seminar groups
12 Hour(s)
Student's own work with learning resources
120 Hour(s)
Sum workload: 
168

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.