MET 1333 Econometrics
This course offers an introduction to econometrics. Econometrics uses statistical methods to quantify economic relationships. These methods are used both in business and economics, as well as in other social sciences. The course has a fundementally international outlook, through the textbook, applications and the subject matter itself. The course teaches digital tools for analyzing data.
After completing the course the students will have:
- Acquired knowledge about the application of regression analysis to quantify causal relationships.
- Acquired knowledge about different regression models.
- An understanding of the depth of the assumptions that must be met in order for the results of empirical analyzes to be interpreted causally.
After completing the course, students will be able to:
- Critically interpret the results of empirical analyzes.
- Discuss strategies for building models that can be used for predictions and policy analysis.
- Master simple linear regression methods, as well as non-linear regression functions (such as logarithmic functions), regression with panel data (combined cross-sectional and time-series data), non-linear probability models (probil and logit), and methods for isolating variation in explanatory variables that are independent of the residual.
- Use regression methods to analyze data using STATA.
Students will be trained to use critical sense of the assumptions on which simple regression analysis is based. In many specific applications, these assumptions break down and they should know what problems this can cause. Students must therefore be open to testing if critical assumptions are in practice and could consider using modified models and methods that better suit the type of data they have available. The students should also be aware that there are causal relationships that are not possible to quantify in practice.
- Simple linear regression
- Multiple linear regression
- Non-linear regression functions
- Internal and external validity
- Regressions using panel data
- Probit and logit regressions
- Regressions with instrumental variables
- Experiments and quasi-experiments
The course consists of 36 hours of lectures and 9 hours of problem-solving.
Students that have not gotten approved the coursework requirements, must re-take the exercises during the next scheduled course.
Students that have not passed the written examination or who wish to improve their grade may re-take the examination in connection with the next scheduled examination.
Higher Education Entrance Qualification
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.
MET 1180 Mathematics, MET 1190 Statistcs or equivalent.
|Mandatory coursework||Courseworks given||Courseworks required||Comment coursework|
|Mandatory||7||5||I løpet av kurset bil det bli gitt syv (7) oppgaver via læringsplattformen It’s learning. Det er et krav at studentene skal ha fått godkjent fem (5) av disse for å kunne gå opp til eksamen. Tilbakemelding på oppgavene vil både skje elektronisk og gjennom oppgavegjennomgang i plenum. Ved kursstart vil det bli opplyst om tidsfristene for arbeidskravene. Arbeidskravene krever bruk av STATA.|
|Comment coursework:||I løpet av kurset bil det bli gitt syv (7) oppgaver via læringsplattformen It’s learning. Det er et krav at studentene skal ha fått godkjent fem (5) av disse for å kunne gå opp til eksamen. Tilbakemelding på oppgavene vil både skje elektronisk og gjennom oppgavegjennomgang i plenum. Ved kursstart vil det bli opplyst om tidsfristene for arbeidskravene. Arbeidskravene krever bruk av STATA.|
|Exam category||Weight||Invigilation||Duration||Support materials||Grouping||Comment exam|
Form of assessment:
Internal and external examiner
Examination every semester
|Form of assessment:||Written submission|
|Support materials:|| |
|Resit:||Examination every semester|
Feedback activities and counselling
Prepare for teaching
Group work / Assignments
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.