GRA 6039 Econometrics with Programming
GRA 6039 Econometrics with Programming
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.
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.
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.
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.
- Review of probability and basic statistics.
- Multiple linear regression.
- Time series models.
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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.
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.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group (1 - 3) Duration: 1 Week(s) Exam code: GRA60393 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: Final written examination Exam code: GRA60394 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) | Lectures |
Teaching | 6 Hour(s) | Practical Matlab-work under supervision. |
Examination | 15 Hour(s) | Home-exam related work. |
Examination | 3 Hour(s) | Final exam |
Student's own work with learning resources | 100 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.
An oral defense of the assignment might be required.