2531.18 - Econometrics
Course number
2531.18
Title
Econometrics
ECTS
7.5
Prerequisites
Pass grade in Mathematics B at the upper secondary level.
Skills and knowledge from or equivalent to those taught in the
courses Probability and Statistics, Mathematics 1 and
Mathematics 2 is an advantage.
Purpose
The aim of the course is to provide an introduction to the nature
and use of empirical investigation in economics. In addition, the
course aims to provide insight into the assumptions underlying
these empirical investigations, as well as the problems that may
arise in such investigations.
Content
The course examines in detail the multiple linear regression model
in the context of cross sectional data. It considers the estimation
technique Ordinary Least Squares (OLS), as well as crucial
properties like unbiasedness and consistency of OLS.
The course also examines situations where the classical
assumptions do not hold. It emphasises the problems of
heteroscedasticity and correlation between the error term and one
or several explanatory variables.
Learning and teaching approaches
New material is presented in lectures and classes. In classes,
students solve and present exercises. STATA (or equivalent
program) is used extensively.
Learning outcomes
Having successfully completed the course, the student will have
knowledge of the following:
- simple linear regression model and the classical
assumptions underlying the model
- multiple regression model and the assumptions underlying
the model
- statistical inference in the context of regression analysis
- when it is appropriate to talk about a causal relationship
between variables.
Having successfully completed the course, the student will be able
to:
- derive simple estimators and statistical properties of these
estimators (such as unbiasedness and consistency)
- use different estimation techniques, where emphasis is
placed on Ordinary Least Squares (OLS), Weighted Least
Squares (WLS), Instrumental Variables (IV) and Two Stage
Least Squares, and discuss when it is appropriate to use
these different estimators.
- test various hypotheses concerning the population
parameters
- calculate and interpret p-values and confidence intervals
in the context of regression analysis
- choose between different functional forms
- use dummy variables
- evaluate whether the underlying assumptions are
satisfied, and make appropriate changes when they are
not satisfied
Assessment method
3 hour written exam.
Students may use textbooks, notes and data programs during the
exam. They may not use the internet.
Students are required to pass a minimum of two assignments in
order to attend the exam.
Examination
Internal
Marking scale
7-
Bibliography
TBA
Contact
Herit Vivi Bentsdóttir Albinus