| 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 |
The course is a specialisation option in
econometrics progression line and bases on a core econometrics
course such as BA-BMECV1031U Økonometri or KAN-COECO1058U
Econometrics.
The course material has a high technical level. 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. Students can choose between R and
Stata. |
| Examination |
|
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 |
| Release of assignment |
Subject chosen by students themselves, see
guidelines if any |
| 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 topics 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 |
|
Lectures:
Lecture notes.
Selected scientific articles to be specified
during the course.
Further recommended readings, revision material and articles
will be posted on Canvas.
Textbooks:
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".
|
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