GRA 6652 Research Methodology - Economics
GRA 6652 Research Methodology - Economics
This course is identical to GRA 6648 (previous course code).
This course is a graduate level introduction to Econometrics.
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.
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:
- Understand strategies of information harvesting, including search techniques and critical evaluation of sources
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.
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
Lectures and practical exercises that must be solved on the computer using Matlab/R or similar software.
It is the student’s own responsibility to obtain any information provided in class.
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 |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Grouping: Group/Individual (1 - 3) Duration: 1 Week(s) Comment: Library assignment Exam code: GRA 66521 Grading scale: Pass/fail Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 30 Grouping: Group (2 - 3) Duration: 1 Semester(s) Comment: 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 code: GRA 66522 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: School Exam Form of assessment: Written School Exam - pen and paper Exam/hand-in semester: First Semester Weight: 70 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: GRA 66523 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Seminar groups | 12 Hour(s) | |
Student's own work with learning resources | 80 Hour(s) | |
Submission(s) | 32 Hour(s) |
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.