ECON9100 – Advanced Econometrics
This course will be revised during 2017. Students who plan to take the revised version of ECON5100/9100, ECON5106/ECON9106, in the autumn 2017 are strongly recommended to take ECON4137 in the spring semester 2017 as this will be the recommended prerequisite for the new version of ECON5100/9100.
This course introduces core microeconometric methods and the principles of causal inference. We will cover instrumental variables, elementary panel data models, and limited dependent variable models. Both experimental and quasi-experimental approaches to causal inference and program evaluation will be covered.
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.
Students will also be introduced to statistical computing with Stata, a statistical package for data analysis, data management, and graphics.
- 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 applied microeconometric literature, and to perform basic econometric analyses themselves.
- Basic skills in using Stata in performing various analyses on economic data will be developed through exercises and examples in the textbook
- Students should be able to interpret Stata output
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
Students at UiO must apply for courses in StudentWeb.
Please note that the course ECON9100 is offered to PhD candidates at the Department of Economics. Other candidates admitted to a PhD program may apply to take the course, but must be registered at the University of Oslo.
Students at the Master's programme must use the code ECON5100.
Formal prerequisite knowledge
- 10 credits overlap with ECON5100 – Advanced Econometrics (discontinued)
- 10 credits overlap with ECON4136 – Applied Statistical Analysis for the Social Sciences (discontinued)
- 10 credits overlap with ECON301B Applied statistics and econometrics
Lectures: 2 hours per week throughout the semester.
Seminar: 2 hours per week through parts of the semester.
Both the exam and the course portfolio/project must be passed to have the course approved.
Access to teaching
A student who has completed compulsory instruction and coursework and has had these approved, is not entitled to repeat that instruction and coursework. A student who has been admitted to a course, but who has not completed compulsory instruction and coursework or had these approved, is entitled to repeat that instruction and coursework, depending on available capacity.
The examination has two parts. Students must pass both parts to pass the course.
- A 3-hour written school exam, graded pass/fail.
- An independent replication exercise, graded pass/fail.
Examination support material
Open-book exam, where all written and printed resources, as well as calculator, are allowed.
Language of examination
The examination text is given in English, and you submit your response in English.
Students on phd-level are awarded either a passing or failing grade. The pass/fail scale is applied as a separate scale with only two possible results.
Explanations and appeals
Resit an examination
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.