ECON5106 – Advanced Applied Econometrics

Course content

This course has joint teaching with ECON9106B – 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 the master’s thesis

Admission to the course

Students admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

Students not admitted to the Master’s programme in Economics or the Master’s programme in Economic Theory and Econometrics (Samfunnsøkonomisk analyse), can apply for admission to one of our study programmes, or apply for guest student status.

Formal prerequisite knowledge

• Bachelor`s degree in Economics, or equivalent.
• ECON3150 – Introductory Econometrics / ECON4150 – Introductory Econometrics , or equivalent.

Recommended previous knowledge

• Students who do not have ECON4137 – Applied Micro Econometrics or equivalent, are advised not to take this course.

Overlapping courses

• 10 credits overlap with ECON9106 – Advanced Applied Econometrics (discontinued).
• 8 credits overlap with ECON9106B – Advanced Applied Econometrics.
• 5 credits overlap with ECON5100 – Advanced Econometrics (discontinued).
• 5 credits overlap with ECON9100 – Advanced Econometrics (discontinued).
• 5 credits overlap with ECON5103 – Advanced Econometrics - Panel Data (discontinued).
• 5 credits overlap with ECON9103.
• 5 credits overlap with ECON5105.
• 5 credits overlap with ECON9105.

Teaching

Lectures and seminars.

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.

Previous exams (ECON4135, ECON4136, ECON5100 and ECON5106)

Exam papers with comments from examiner

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 scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

Students who would like to have the course approved as part of the requirement for admission to our phd-program, must obtain the grade C or better. Students who would like to have the course approved as a part of our phd-program, must obtain the grade B or better.

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.