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Advanced Microeconometrics

Course content

Advanced Microeconometrics covers principles for estimation and inference for both linear and non-linear parametric models in classical (i.e. low-dimensional) as well as high-dimensional settings. Methods covered include the least absolute shrinkage and selection operator (LASSO), non-linear least squares (NLS), maximum likelihood estimation (MLE) and generalized method of moments (GMM), among others. These methods are discussed in the context of a wide range of microeconometric models. The course aims to provide both the theoretical foundations of these methods, as well as the practical tools to implement them in a relatively low-level programming language (here: Python).

 

The course centers around the following umbrella topics:

 

  1. Classical linear panel data models and methods
  2. The high-dimensional linear model and approaches to high-dimensionality
  3. Classical non-linear models (e.g. for binary or multinomial responses) and methods (e.g. numerical optimization)

 

The course aims to provide the student with a statistical toolbox that can be applied for estimation of and conducting inference in a wide range of reduced-form or structural microeconometric settings.

Education

MSc programme in Economics – elective course

 

The PhD Programme in Economics at the Department of Economics:

  • The course is an elective course with research module. In order to register for the research module and to be able to write the research assignment, the PhD students must contact the study administration AND the lecturer.
  • The course is a part of the admission requirements for the 5+3 PhD Programme. Please consult the 5+3 PhD admission requirements.

 

The course is open to:

  • Exchange and Guest students from abroad
  • Credit students from Danish Universities
  • Open University students
Learning outcome

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

 

Knowledge:

  • Define the data generating process (including model (un)observables and parameters), the appropriate estimation method(s) and the assumptions ensuring consistency (including identification) for treatment of various models.
  • Define the principles of estimation and inference.
  • Identify the most common numerical optimization algorithms and their implementations in Python. 
  • Account for the key opportunities and challenges that arise in models for panel versus cross-sectional data.
  • Discuss merits and drawbacks of different estimators for a specific problem.
  • Discuss validity of model assumptions. For concrete data examples, the student must be able to precisely relate the mathematical assumptions to economic intuition about the behavior that is underlying the data.

 

Skills:

  • Assess which estimator is suitable for a given model.
  • Estimate model parameters through programming in Python.
  • Test formal statistical hypotheses.
  • Replicate, extend and critically discuss microeconometric research.
  • Code an estimator from a research paper up from scratch and conduct estimation and inference.
  • Exploit the added value of panel datasets over purely cross-sectional datasets.
  • Argue for/against an estimation technique using probability theory and (asymptotic) statistics.

 

Competencies:

 

  • Assess which economic research questions can be answered when faced with a new dataset.
  • Independently carry out and present empirical analysis e.g. in the master’s thesis and future jobs.
  • Independently formulate and answer empirical economic questions and economic research question with a given dataset e.g. in a government agency or in the private sector.
  • Initiate, be responsible for and receive constructive feedback in future collaborations.

The course is a combination of lectures, exercise classes and mandatory homework assignments. The lectures cover the theory and the intuition behind the estimators and the methods.

Exercises classes as well as homework assignments span a mix of theoretical, empirical and computational topics and allow students to put theory into practice in both supervised and unsupervised environments.

Homework assignments furthermore allow students to obtain hands-on coding experience by implementing estimators in Python while using real datasets and addressing real economic questions. Students are expected to have (at least) attempted the exercises prior to attending exercise classes.

Jeffrey M. Wooldridge "Econometric Analysis Of Cross Section And Panel Data” 2010 (2nd edition) MIT Press Ltd.

Lecture notes

Pre-requisites are the econometrics course "Econometrics I" at the Bachelor of Economics, University of Copenhagen or similar course.

It is recommended to have followed or concurrently to follow "Econometrics II" at the Studies of Economics, University of Copenhagen - or a similar econometrics course.

Students will be required to do mathematical derivations in order to complete both the exercise classes, homework assignments and exam. So students should have a sound knowledge of linear algebra and calculus (e.g., matrix algebra, differentiation) e.g. from the course "Mathematics B" at the Bachelor of Economics, University of Copenhagen or similar course.

Programming will be an important component of the exercise classes, homework assignments, and exam. Prior experience with Python is not a pre requisite to begin at this course. However, students are strongly encouraged to walk through the first three lectures of ‘Introduction to Programming and Numerical Analysis’ available via https:/​/​numeconcopenhagen.netlify.app/​ before the start of the semester to gain familiarity with the language.

Schedule:
• 2x2 hours and 1x2 hours of lectures alternating every other week.
• 1x3 hours of exercise classes every week.


Schema:
The overall schema for the Master can be seen at KUnet:
MSc in Economics => "Courses and teaching" => "Planning and overview" => "Your timetable"

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). The lectures are shown in each link.

You can find the similar information partly in English at
https:/​/​skema.ku.dk/​ku2223/​uk/​module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-E22; [Name of course]”
-Select Report Type: “List – Weekdays”
-Select Period: “Efterår/Autumn”
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.

Oral
Individual
Collective
Peer feedback (Students give each other feedback)

Assignments handed in for peer feedback will receive written feedback from fellow students based on criteria set up by the lecturers.

If deemed relevant, the lecturers will provide oral collective feedback in lectures based on a sample of the assignments.

 

Office hours: Offered by the lecturer, who will inform the students about the time and place.

ECTS
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 one of the mandatory assignments worked on during the course. The student can use the peer feedback received during the course to improve the assignment. This can be done before the exam period begins. The repeat assignment is 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.
The two parts are weighted equally (50/50) in the overall assessment.

Please be aware that:
- The new assignments can be written individually or by groups of maximum three students.
- The groups and the students, that hand in an individual assignment, are not allowed to communicate with each other about the given problem-set for the new assignment.
- 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.
Exam registration requirements

To qualify for the exam the student must no later than the given deadlines during the course:

  • Hand in a minimum of 2 out of 3 mandatory assignments.
  • Provide useful written peer feedback based on specific criteria for a minimum of 2 out of 3 mandatory assignments to two students from other groups.

 

Please be aware:

  • The teaching assistant and/or lecturer control the assignments and the feedback.
  • The assignments can be written individually or by groups of maximum three students.
  • The peer feedback must be written individually.
  • The plagiarism rules and the rules for co-written assignments must be complied.
  • The assignments and the peer feedback must be written in English.
  • The mandatory assignments and the peer feedback are part of a portfolio exam. See “Type of assessment”
Aid

All aids allowed for the written exams.

 

Use of AI tools is permitted. You must explain how you have used the tools. When text is solely or mainly generated by an AI tool, the tool used must be quoted as a source.

 

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.
Exam period

Exam information:

The examination date can be found in the exam schedule  here

More information is available in Digital Exam from the middle of the semester. 

More information about examination, rules, aids etc. at Master (UK) and Master (DK).

Re-exam

The reexam is a 20 min. oral exam with 20 min. preparation time.

All written aids allowed during the preparation time. Notes made during the preparation allowed at the examination.

The questions cover the entire curriculum and are based on the written mandatory assignments.

 

To qualify for the exam the student must no later than the given deadlines during the course:
• Hand in a minimum of 2 out of 3 mandatory assignments
• Provide useful written peer feedback based on specific criteria for a minimum of 2 out of 3 mandatory assignments to two students from other groups.

 

Reexam info:

The reexamination date/period can be found in the reexam schedule  here

Exact day, time and place: See Digital Exam in February. 

More info: Master(UK) and Master(DK)

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
  • 42
  • Class Instruction
  • 24
  • Preparation
  • 92
  • Exam
  • 48
  • English
  • 206

Kursusinformation

Language
English
Course number
AØKA08084U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Ph.D.
Duration

1 semester

Placement
Autumn
- Go to 'Signup' for information about registration and enrollment.
Price

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

Schedulegroup
and venue:
- For teaching: Go to 'Remarks'.
- For exam and re-sits: Go to 'Exam'.
Studyboard
Department of Economics, Study Council
Contracting department
  • Department of Economics
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Jesper Riis-Vestergaard Sørensen   (4-6f777b78456a68747333707a336970)
Teacher

See 'Course Coordinators'

Teachers of exercise classes:
Exercise Class 1:
Exercise Class 2:
Exercise Class 3:

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

Saved on the 24-09-2024
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