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2022/2023  KAN-CMECV1249U  Panel Econometrics

English Title
Panel Econometrics

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for HA/cand.merc. i erhvervsøkonomi og matematik, MSc
Course coordinator
  • Ralf Andreas Wilke - Department of Economics (ECON)
Main academic disciplines
  • Mathematics
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 10-02-2022

Relevant links

Learning objectives
  • Understand econometric methods of estimation and inference for panel data
  • Understand proofs and derivations in matrix notation
  • Choosing an econometric model, form those introduced in the course, and explaining why it is the suitable model for the specific situation
  • Interpret estimation results in R/STATA output correctly and comment on appropriateness of their presentation.
  • Relate R/STATA code and R/STATA output to the econometric models introduced in the course.
  • Competence in R or STATA to do econometric analysis with the introduced models of the course.
Course prerequisites
BA-BMECV1031U Økonometri
KAN-COECO1058U Econometrics

The course has a high technical level and is a progression course. Students are expected to have knowledge of the statistical properties of ordinary least squares (OLS), (feasible) generalised least squares (FGLS) and two stage least squares (2SLS) and maximum likelihood (ML) estimation, as well as hypotheses tests about parameters in regression analysis and robust inference (heteroscedasticity, serially related errors). Knowledge of matrix algebra, fundamentals of probability and mathematical statistics are required. Basic knowledge of either R or STATA.
Panel Econometrics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Please see text below
Maximum 10 pages, excluding references and appendices.
The main textbody must not exceed 10 pages and should contain main result tables and figures which are required for the understanding of the main text. References and appendices with additional material (additional results or figures, code or proofs/derivations) do not count for the page limit but should not be too excessive.
Assignment type Project
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

Students do an unsupervised project work and submit a project report that is assessed. The analysis in the project should use one or more methods that have been covered in the course. The project can be methodological (by for example modyfying or extending an approach and/or by doing simulations to investigate properties) or it can be empirical (by applying the methods to an empirical problem) if the student has interesting data at hand. The topic of the project is to be suggested by the student and has to be approved upfront by the assessor.

Course content, structure and pedagogical approach

We start with crafting and estimation of a system of equations including simultaneous equations and seemingly unrelated regression by 2SLS/3SLS and GMM.
We then introduce the main static linear panel models: Pooled OLS, FE,RE,FD before they are combined with IV methods (FD-IV, FE-IV and RE- IV, Hausman-Taylor type models). We study various topic in relation to linear panel models such as dynamic panel models (Anderson-Hsiao/ System 2SLS, Arrelano-Bond/ GMM) and measurement error in variables. We then move to nonlinear panel models. Here we extend ML estimation to cope with dependent observations (using Kullback-Leibler Information criterion) and consider methods to correct estimated standard errors and statistics. This is then presented in detail for the binary dependent variable panel model. The course concludes by considering sample selection issues in form of panel attrition and unbalanced panels.

Description of the teaching methods
The course comprises of 25 hours of lectures and 8 hours of computer classes. The first computer class is an introductory class. The following classes cover problem sets.
Feedback during the teaching period
1) Office hours.
2) Computer classes: students are encouraged to present their solutions to problem sets to receive formative feedback.
Student workload
Lectures 30 hours
Preparation 90 hours
Project 86 hours
Expected literature


    Lecture notes.
    Selected scientific articles to be specified during the course.

Further recommended readings, revision material and articles will be posted on Canvas.


Wooldridge (MIT, 2010) "Econometric Analysis of Cross Section and Panel Data"

Cameron and Trivedi (Cambridge, 2005) "Microeconometrics".


Croissant and Millo (Wiley, 2018) "Panel Data Econometrics with R".

Last updated on 10-02-2022