Statistics, Econometrics, Intermediate Course, 7.5 credits

Learning outcomes

Knowledge and understanding

After completion of the course, the student will have

• deeper knowledge of basic concepts in econometrics
• knowledge of regression models using cross-sectional data and panel data.

Competence and skills

After completion of the course, the student will have

• the ability to use the knowledge in applied situations supported by statistical software.

Judgement and approach

After completion of the course, the student will have

• the ability to critically review and evaluate econometric models.

Content

• Basic concepts in econometrics: model, non-observable heterogeneity, endogeneity
• Simple and multiple linear regression using cross-sectional data
• Regression models for binary response
• Regression modelling for panel data
• Instrumental variables estimation.

Examinations and grades

Written Examination, 6 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Computer Labs, 1.5 credits (Code: A002)
Grades used are Fail (U) or Pass (G).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Comments on grades

To obtain a passing grade for the course as a whole, a passing grade is required on all course components. The final grade for the entire course is a function of the grades of the course components. Detailed information on the requirements for different grade levels is given at the course start.

Modes of assessment

Written Examination, 6 credits (Code: A001)

Computer Labs, 1.5 credits (Code: A002)

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

Successful completion of at least 12 credits within the course Basic Statistics, 15 credits and 3 credits within the course Data mining and business analytics, 15 credits or
Successful completion of at least 12 credits within the course Basic Statistics, 15 credits and 1.5 credits within the course Regression Analysis, 7.5 credits.

For further information, see the university's admission regulations.

Other provisions

Teaching language is English provided that at least one student does not speak Swedish. Otherwise teaching language may be Swedish.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Reading list and other learning resources

Stock, James H. & Mark W. Watson, 2020, Global Edition, 4th Edition (latest edition)
Introduction to Econometrics
Pearson Education, 800 pages, approx 340 pages to be read