ECON9106B – Advanced Applied Econometrics
Course description
Course content
This course has joint teaching with ECON5106 – Advanced Applied Econometrics.
This course introduces core microeconometric methods for estimation and inference, Advanced causal inference, and the analysis of dynamic outcomes. The emphasis will be on developing a solid understanding of the underlying econometric principles of the methods taught, as well as on their empirical application using Statistical software (Stata/R).
Learning outcome
Knowledge outcomes
The course develops knowledge of both the formal and practical aspects of important microeconometric methods. The successful student will be able to understand when to apply a method, how to apply this method and the method’s limitations. This also covers model specification and being able to correctly interpret estimation results. Mastering the course’s content will allow students to understand much of the advanced applied microeconometric literature, and to implement the econometric analyses themselves.
Skills
Skills in using Stata/R in performing relevant analyses on economic data will be developed through exercises and examples in the textbook. Students should be able to interpret estimation output.
Competence
You should be able to read and understand project reports and journal articles that make use of the concepts and methods that are introduced in the course make use of the course content in your own academic work, for example in analyses that are part of your PhD thesis.
Admission to the course
This course is offered to PhD candidates at the Department of Economics. Other candidates admitted to a PhD program may apply to take the course.
Formal prerequisite knowledge
- ECON3150 – Introductory Econometrics / ECON4150 – Introductory Econometrics , or equivalent.
Overlapping courses
- 8 credits overlap with ECON9106 – Advanced Applied Econometrics (discontinued).
- 8 credits overlap with ECON5106 – Advanced Applied Econometrics.
Teaching
Lectures and seminars.
The course responsible can at the beginning of the semester update the syllabus list by changing no more than 3 articles, though in a way that it will not change the overall scope or thematic content of the course.
The syllabus also includes any lecture notes that will be made available for the students in Canvas
Examination
Students will be evaluated by means of a portfolio assessment?which consists of three parts:
- ?Part 1 will be to replicate a recently-published research paper that uses econometric methods covered in the course.
- Part 2 will be to implement an extension to that paper using what has been learned throughout the course.
- Part 3 will involve a series of exam questions that are related to the research paper replicated and extended by the students.
Students must pass all three parts in the same semester to pass the course.
?
Examination support material
Resources allowed: Open book examination, where all printed and written resources are allowed.?Generating all or part of the exam answer using AI tools such as Chat GPT or similar is not allowed.
Language of examination
The examination text is given in English, and you submit your response in English.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
Resit an examination
If you are sick or have another valid reason for not attending the regular exam, we offer a postponed exam later in the same semester.
There are restrictions on resitting this exam. See further information about resitting an exam.
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.