Syllabus for Advanced Econometrics

Avancerad ekonometri

Syllabus

  • 7.5 credits
  • Course code: 2ST123
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Statistics A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2019-09-10
  • Established by:
  • Revised: 2021-09-10
  • Revised by: The Department Board
  • Applies from: week 03, 2022
  • Entry requirements: 120 credits including 90 credits in statistics.
  • Responsible department: Department of Statistics

Learning outcomes

After completing the course the student is expected to:
know and be able to use software for the purpose of analysing econometric models and economic data,
have a solid knowledge about statistical theory and methodology, especially regarding economic applications,
be able to estimate and interpret econometric models.

Content

Regression analysis, estimation and inference, properties
assumption for estimation, consequences and detection.
Asymptotics for estimators and tests
Resampling
Multivariate regression
Instrumental variable
Binary dependent variables
GMM

Instruction

Instruction is given in the form of in-class lectures, computer exercises, and seminars.

Assessment

The examination takes place through a written examination and/or through written and/or oral presentation of take-home assignments.

"If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator."

Other directives

The course is included in the Master´s programme in statistics.

Reading list

Reading list

Applies from: week 03, 2022

Some titles may be available electronically through the University library.