Econometrics II

Course content

Econometrics II gives a detailed account of principles for estimation and inference based on the likelihood function and based on generalized method of moments estimation with application to cross-sectional data and time series data.


In addition, Econometrics II presents the econometric analysis of time series data, applying the concepts of non-stationarity, unit roots, co-integration, vector autoregressions, and autoregressive conditional heteroskedasticity (ARCH).


As an integral part of the course, students will learn how to carry out, present, and discuss an empirical analysis on their own.


MSc programme in Economics - mandatory course, if not passed before.


Bacheloruddannelsen i økonomi – Prioriteret valgfag på 3. år (angivet med et p).

The Danish BSc programme in Economics - prioritized elective at the 3rd year (symbolized by ‘p’).

Learning outcome

After completing the course the student is expected to be able to:



  • Account for the different principles for estimation and inference – specifically the method of maximum likelihood, the (generalized) method of moments – and discuss relative advantages and drawbacks.
  • Give a precise definition and interpretation of the concept of stationarity of time series data.
  • Describe the conditions for consistent estimation and valid inference in a statistical model.
  • Give a precise definition of the concept of unit roots.
  • Explain the consequences of unit roots in economic time series data.
  • Interpret statistical models for stationary and non-stationary time series.
  • Give a precise definition and interpretation of the concepts cointegration and error correction
  • Account for statistical models based on cointegration and error correction.
  • Give a precise definition and interpretation of the concept of autoregressive conditional heteroskedasticity (ARCH).
  • Account for statistical models with ARCH in financial time series.



  • Identify the characteristic properties of a given data set of economic time series and suggest and construct relevant statistical models.
  • Derive estimators of the statistical model’s parameters using the principles of method of moments and maximum likelihood. Estimate and interpret the parameters.
  • Construct misspecification tests and analyze to what extent a statistical model is congruent with the data.
  • Construct statistical tests for unit roots in economic time series.
  • Construct statistical tests for cointegration in economic time series.
  • Formulate economic questions as hypotheses on the parameters of the statistical model and test these hypotheses.
  • Use statistical and econometric software to carry out an empirical analysis.
  • Present a statistical model and empirical results in a clear and concise way. This includes using statistic and econometric terms in a correct way, giving statistically sound and economically relevant interpretations of statistical results, and presenting results in a way so that they can be reproduced by others.



  • Choose the relevant statistical model given the characteristics of a given data set of economic time series and apply the statistical tools to carry out, present, and discuss an empirical analysis and test specific economic hypotheses.
  • Read and critically evaluate research papers containing applied econometric time series analyses.

Lectures and exercise classes.

Activities to challenge and activate students, such as in quizzes and peer-discussions, are used in lectures and as preparation. The exercise classes are both theoretical and applied with written assignment covering important topics in the course. If possible some of the exercise classes will be organized as workshops with all students together.

Changes to teaching methods due to a pandemic crisis:
The teaching in this course might be changed to either fully or partly online due to a pandemic crisis. If changes are implemented please read the study messages at KUnet or the announcements in the virtual course room on Absalon (for enrolled students).

Marno Verbeek: A Guide to Modern Econometrics, 5th Ed., Wiley. ISBN 978-1-119-472117.

Lecture notes.

The course requires knowledge equivalent to that achieved in "Probability Theory and Statistics" and "Econometrics I" at the Bachelor of Economics, University of Copenhagen.


Autumn 2022:
2x2 hour lectures each week from week 36 to 50 (except week 42).
2 hours of workshops/exercise classes from week 36/37 to 50 (except week 42).

Spring 2023:
2x2 hour lectures each week from week 6 to 20 (except holidays).
2 hours of workshops/exercise classes each week from week 6/7 to 20 (except holidays).

The overall schema for the BA 3rd year and Master can be seen at KUnet:
MSc in Economics => "Courses and teaching" => "Planning and overview" => "Your timetable"
BA i Økonomi/KA i Økonomi => "Kurser og undervisning" => "Planlægning og overblik" => "Dit skema"

Timetable and venue:
To see the time and location of lectures and exercise classes please press the link/links under "Timetable"/​"Se skema" at the right side of this page (E means Autumn, F means Spring). The lectures is shown in each link.

You can find the similar information in English at
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-E22; [Name of course]” or “2200-F23; [Name of course]”
-Select Report Type: “List – Week Days”
-Select Period: “Efterår/​Autumn" or “Forår/Spring”
Press: “ View Timetable”

Please be aware:
- The study administration allocates the students to the exercise classes according to the principles stated in the KUnet.
- If too many students have wished a specific class, students will be registered randomly at another class.
- It is not possible to change class after the second registration period has expired.
- If there is not enough registered students or available teachers, the exercise classes may be jointed.
- The student is not allowed to participate in an exercise class not registered.
- The teacher of the exercise class cannot correct assignments from other students than the registered students in the exercise class except with group work across the classes.
- All exercise classes are taught in English and it is expected that the students ask questions in English, so foreign students are included in the dialog.
- The schedule of the lectures and the exercise classes can change without the participants´ acceptance. If this occur, you can see the new schedule in your personal timetable at KUnet, in the app myUCPH and through the links in the right side of this course description and at the link above.
- It is the students´s own responsibility continuously throughout the study to stay informed about their study, their teaching, their schedule, their exams etc. through the curriculum of the study programme, the study pages at KUnet, student messages, the course description, the Digital Exam portal, Absalon, the personal schema at KUnet and myUCPH app etc.

Continuous feedback during the course
Peer feedback (Students give each other feedback)


Students will receive written feedback at the assignments through peerfeedback. Students will rate the feedback received.

The lecturer will, if deemed relevant, provide oral collective feedback in lectures based on a sample of the assignments. Continuous feedback is available from online review quizzes in the lectures and from teaching assistants in exercise classes.


Office hours: Time and place will be informed by the lecturer in Absalon.

7,5 ECTS
Type of assessment
Portfolio, 48 hours
Type of assessment details
The exam is a written assignment consisting of two parts:
• Part 1: The first part is based on two of the mandatory assignments worked on during the course. The student can use the peer feedback received during the course to improve these assignments. The repeat assignments are chosen at random and reveals with the release of the exam.
• Part 2: The second part is a new assignment given in English. It takes approximately 24 hours to answer the new assignment.

In the assessment part one is weighted approximately 2/3 of the grade and part two is weighted approximately 1/3. The overall grade depends on the overall impression of the answer.

Please be aware that:
• The new assignment can be written individually or by groups of maximum three students.
• The plagiarism rules and the rules for co-written assignments must be complied.
• All parts must be answered in English
• All parts must be uploaded to Digital Exam in one file.

All aids are allowed for the written exams.

For further information about allowed aids for the re-examination, please go to the section "Re-exam".

Marking scale
7-point grading scale
Censorship form
No external censorship
for the written exam.
The oral re-examination may be with external assessment.
Criteria for exam assessment

Students are assessed on the extent to which they master the learning outcome for the course.


In order to obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.


In order to obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of  the knowledge, skills and competencies listed in the learning outcomes.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 56
  • Class Instruction
  • 28
  • Preparation
  • 74
  • Exam
  • 48
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 semester

Autumn And Spring
- Go to 'Signup' for information about registration and enrollment.

Information about admission and tuition fee:  Master and Exchange Programme, credit students and guest students (Open University) 

and venue:
- For teaching: Go to 'Remarks'.
- For exam and re-sits: Go to 'Exam'.
Department of Economics, Study Council
Contracting department
  • Department of Economics
Contracting faculty
  • Faculty of Social Sciences
Course Coordinators
  • Heino Bohn Nielsen   (18-716e727778376b7871773777726e757c6e77496e6c787737747e376d74)
  • Rasmus Søndergaard Pedersen   (3-777875456a68747333707a336970)

See ‘Course Coordinators’

Teaching assistants:
Autumn 2022:
Exercise class 1:
Exercise class 2:
Exercise class 3:
Exercise class 5:
Exercise class 6:
Exercise class 7:
Exercise class 9:
Exercise class 10:

Spring 2023:
Exercise class 1:
Exercise class 2:

Please read "Remarks" regarding the schedule of the teaching.

Saved on the 31-10-2022

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Courseinformation of students