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: 
2024 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

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

  • 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 using Matlab/R or similar software. 

Software tools
Matlab
R
Additional information

Continuous assessment will no longer exist as an examination form from autumn 2023. For questions regarding previous results, contact InfoHub.

It is the student’s own responsibility to obtain any information provided in class.

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.

Assessments
ExamWeightInvigilationDurationSupport materialsGroupingComment
Exam category
Submission
Form of assessment
Written submission
Exam code
GRA 66487
Grading scale
Pass/fail
Grading rule
Internal examiner
Resit
Examination when next scheduled course
No
1 Week(s)
Group/Individual (1 - 3)
Library assignment
Exam category
Submission
Form of assessment
Written submission
Exam code
GRA 66488
Grading scale
ECTS
Grading rule
Internal examiner
Resit
Examination when next scheduled course
40
No
1 Semester(s)
Group (2 - 3)
Written report consisting of 3-4 assignments given throughout the semester. Requires Matlab/R. Students will be given the opportunity to present and get feedback on their work during the semester and before submitting the report.
Exam category
Submission
Form of assessment
Written submission
Exam code
GRA 66489
Grading scale
ECTS
Grading rule
Internal examiner
Resit
Examination when next scheduled course
60
Yes
3 Hour(s)
Individual
Final written examination under supervision
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)
Seminar groups
12 Hour(s)
Student's own work with learning resources
80 Hour(s)
Submission(s)
32 Hour(s)
Sum workload: 
160

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.