Advanced Microeconometrics
Course content
Advanced Microeconometrics gives a detailed account of principles for estimation and inference based on the most conventional methods for estimation of both linear and non-linear parametric models, such as non-linear least squares, maximum likelihood estimation (MLE) and generalized method of moments (GMM). These methods are used to estimate a range of microeconometric models for panel and cross-sectional data covered in the course. The course provides both the theoretical foundations of these estimation methods, as well as the practical tools to implement them in a relatively low-level programming language (Python).
The course will be developed along the following four axes:
1) Linear unobserved effects panel data models:
- Estimation with strictly exogenous regressors.
- Random and fixed effects, first differences.
- GMM estimation of dynamic models with sequentially exogenous regressors.
2) Estimation methods and numerical tools for non-linear parametric models:
- M-estimators (e.g., NLS, MLE, LAD) and two-step M-estimators.
- Generalized Method of Moments (GMM) and Minimum Distance (MD).
- Simulation-based estimation methods.
- Numerical optimization algorithms (e.g., Nelder-Mead, Newton-Raphson, BHHH).
3) Discrete-outcome models and models for demand:
- Binary choice models for cross-sectional and panel data
- Multinomial choice models (e.g., logit, nested logit, probit, mixed logit)
- Censoring, selection models (e.g., tobit-type models)
4) Traditional semi-/non-parametric methods and introduction to machine learning:
- Kernel and series regression.
- Regularized linear regression (e.g., LASSO, Ridge).
The course will provide the student with a statistical toolbox that can be used for estimation of and inference in a wide range of reduced-form and structural microeconometric models.
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.
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 the models in axes 1, 3 and 4.
- Define the principles of estimation and inferences for the paradigms in axis 2.
- 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.
- Exploite the added value of panel datasets over purely cross-sectional datasets.
- Master estimation 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 colaborations.
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.
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).
Jeffrey M Wooldridge "Econometric Analysis Of Cross Section And Panel Data” 2010 (2nd edition) MIT Press Ltd.
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.
NOTE: The following recommended requirement is deleted from 16th of
August 2022: However students are strongly encouraged to complete
the short course “Online Python Course for Students of Economics”
(available on Absalon at bit.ly/3cX4nXm) before the start of the
semester.
NOTE: New recommended requirement is:
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:
3 hours lectures a week from week 36 to 50 (except week 42).
2 hours exercise classes a week from week 36/37 to 50 (except week
42).
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.
Assignments handed in for anonymous 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: Are offered by Anders Munk-Nielsen and Jesper Riis-Vestergaard Sørensen, who inform day and time.
for enrolled students: Rules etc at Master(UK) and Master(DK)
Foreign students and guests: Information about admission requirements, application, tuition fee, registration at Study Economics. Please read the curriculum before enrolment.
Efteruddannelse, gæste- og enkelfagsstuderende: Ansøgning, optag, pris etc. se Uddannelse i Økonomi. Læs venligst studieordningen inden tilmelding.
- 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. - Aid
-
All aids 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
- 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 Coordinators
- Jesper Riis-Vestergaard Sørensen (4-7b838784517674807f3f7c863f757c)
- Anders Munk-Nielsen (3-717d7e5075737f7e3e7b853e747b)
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.
Are you BA- or KA-student?
Courseinformation of students