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), and maximum likelihood estimation (MLE), 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 an imperative programming language (here: Python).
The course centers around the following umbrella topics:
- Classical linear panel data models and methods
- The high-dimensional linear model and approaches to high-dimensionality
- 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.
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
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.
Competences:
- 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 to concurrently follow the
UCPH Economics course "Econometrics II" (or a similar
course).
Students will be required to do mathematical derivations in order
to complete both the exercise classes and homework assignments.
Specifically, students should have a sound knowledge of
• Basic probability theory and statistics at the level of the UCPH
Economics course “Probability theory and statistics”); and,
• Linear algebra and analysis (e.g. matrix calculus,
differentiation, integration, optimization) at the level of the
UCPH Economics course “Mathematics B”.
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.
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.
for enrolled students: Rules etc at Master(UK) and Master(DK)
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
- Exchange and guest students from abroad – sign up through Mobility Online and Selfservice- read more through this website.
- Credit students from Danish universities - sign up through this website.
- Open University students - sign up through this website.
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination, 20 min
- Type of assessment details
- Without preparation.
The oral exam will be based on one of the mandatory assignments/projects, chosen at random by the examiner. The identity of the project chosen will be revealed to the student at the onset of the exam. The student will give a brief oral presentation of their own work and project report, followed by questions from the examiner(s). The conversation will branch out to relevant parts of the curriculum, depending on the project.
Note: While the level of the questions asked hinges on the quality of the chosen project report, the assessment itself will be based on the oral presentation (not the report itself). - Examination prerequisites
-
To qualify for the exam the student must no later than the given deadlines during the course:
- Hand in a minimum of 3 out of 3 mandatory assignments (to be approved by the instructor).
- Provide useful written peer feedback based on specific criteria for a minimum of 3 out of 3 mandatory assignments to two 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
- No aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- 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
-
Same as the ordinary exam.
Reexam information:
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
- 140
- English
- 206
Kursusinformation
- Language
- English
- Course number
- AØKA08084U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
Ph.D.
- Duration
-
1 semester
- Placement
- Autumn
- Price
-
Information about admission and tuition fee: Master and Exchange Programme, credit students and guest students (Open University)
- 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'
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