# 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.

Education

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

Bacheloruddannelsen i økonomi - Obligatorisk fag på 3. år

Rettet 16/11-2020:

Nedenstående gælder først fra Efteråret 2022:

Bacheloruddannelsen i økonomi – Prioriteret valgfag på 3. år (angivet med et p). Vedr prioriterede valgfag, se studieordningen 2020.

Learning outcome

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

Knowledge:

• 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.

Skills:

• 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.

Competencies:

• 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. Some of the exercise classes will be organized as workshops with all students together.

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

Pandemic:
In case of a pandemic like Corona the teaching in this course may be changed to be taught either fully or partly online. For further information, see the course room on Absalon.

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 Study of Economics, University of Copenhagen.

Schedule:

Autumn 2020:
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 2021:
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
https:/​/​skema.ku.dk/​ku2021/​uk/​module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-E20; [Name of course]” or “2200-F21; [Name of course]”
-Select Report Type: “List – Week Days”
-Select Period: “Efterår/Autumn – Weeks 31-4” or “Forår/Spring – Week 5-30”
Press: “ View Timetable”

Please be aware of the rules regarding exercise classes:
- The schedule of the exercise classes is only pre-planned and the schedule can change until the teaching begins without the participants´ acceptance. If this happens, you can see the new schedule in your personal timetable at KUNet, in the app myUCPH and at the links in the right side/the link above.
- That the study administration allocates the students to the exercise classes according to the principles stated at 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.
- The exercise classes may be jointed, if there is not enough registered students or available teachers to teach the classes.
- 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.
- That it is the students´s own responsibility to continuously update themselves about their studies, their teaching, their schedule, their exams etc. through the study pages, the course description, the Digital Exam portal, Absalon, KUnet, myUCPH app, the curriculum etc.

Written
Oral
Individual
Collective
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)

Students will receive written feedback at the assignments from two fellow students based on criteria set up by the lecturer. 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.

ECTS
7,5 ECTS
Type of assessment
Portfolio, 48 hours
The exam is a written assignment consisting of three parts:
- Part 1 and 2 are based on 2 of the assignments worked on during the course. The student can use the received peer feedback to improve the assignments. The repeat assignments are chosen at random and reveals with the release of the exam.
- Part 3 is a new assignment.

All three parts must be uploaded to the Digital Exam portal in one file.

The assignments can be written individually or by groups of maximum three students.

The plagiarism rules must be complied and please be aware of the rules for co-written assignments.

The assignments must be written in English.

It takes approximately 24 working-hours to answer the new assignment.
__
Aid
All aids allowed

__

Marking scale
Censorship form
No external censorship
for the written exam.
__
##### Criteria for exam assessment

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

To receive the top grade, 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.

ECTS
7,5 ECTS
Type of assessment
Portfolio, 48 hours
The exam is a written assignment consisting of three parts:
- Part 1 and 2 are based on 2 of the assignments worked on during the course. The student can use the received peer feedback to improve the assignments. The repeat assignments are chosen at random and reveals with the release of the exam.
- Part 3 is a new assignment.

All three parts must be uploaded to the Digital Exam portal in one file.

The assignments can be written individually or by groups of maximum three students.

The plagiarism rules must be complied and please be aware of the rules for co-written assignments.

The assignments must be written in English.

It takes approximately 24 working-hours to answer the new assignment.

In the event that the summer exams have to be conducted as online exams due to COVID-19, the exam form for the re-exam will be online orally without aids
__
Aid
All aids allowed

for the regular written exam.

__

Marking scale
Censorship form
No external censorship
for the written exam.
__
##### Criteria for exam assessment

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

To receive the top grade, 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.

Single subject courses (day)

• Category
• Hours
• Lectures
• 56
• Class Instruction
• 28
• Preparation
• 98
• Exam
• 24
• English
• 206