2301 - Econometrics 2
Course information
Title
Econometrics 2
Course number
2301.22
Academic year
2024-2025
ECTS
10.00
Level
Bachelor
Faculties
History and Social Sciences
Educations
BSc in Economics and Management
Prerequisites
Pass grade in Econometrics 1 or similar course.
Language of instruction
Faroese
Registration
Students on the fifth semester of B.Sc. in Economics and Business Administration apply to the course on Moodle. Applicants for an individual course must apply via the Student Service Center at lss@setur.fo
Beginning date
Wednesday, August 28, 2024
End date
Thursday, November 28, 2024
Academic content
Purpose
The aim of the course is to introduce different techniques for analysing cross sectional data, time series data and panel data. Furthermore, the aim is for students to learn to read and evaluate conclusions in econometric papers.
Learning outcomes
Having successfully completed the course, the student will have knowledge of the following: - different approaches to analysing cross sectional data, time series data and panel data. - endogeneity in cross sectional data; why it occurs and how we deal with this problem. - necessary assumptions when we analyse time series data - concepts such as stationarity, unit roots, cointegration and Error Correction Models. - simultaneous equations Having successfully completed the course, the student will be able to: - use different estimation techniques: Instrumental variables and 2SLS (IV/2SLS), Differences-in-Differences (DD), First Differences (FD), Fixed Effects (FE) and Random Effects (RE). - determine which estimation technique is appropriate in different circumstances. - use different tests in the context of time series analysis, - estimate limited dependent variable models. - discuss the properties of OLS under measurement error. - present and discuss econometric results.
Content
We discuss in further detail the problem of endogeneity in econometric models and possible solutions. The course gives a detailed account of the econometric analysis of time series data. We introduce concepts such as stationarity, unit roots and autoregressive conditional heteroscedasticity (ARCH).
Learning and teaching approaches
We present new material in lectures and classes. In classes, students solve and present exercises. As an integral part of the course, students are introduced to statistical tools for analysing different data sets and the students will learn how to carry out, present and discuss an empirical analysis.
Assessment
Assessment method
A weighted mark which consists of the following: - midterm (25%) - three-hour written exam (75%) Students may use textbooks, notes and data programs during the exam. They may not use the internet. In the event of a student having to resit this course, the midterm mark will not be carried forward. In this case, the 3 hour written exam will account for 100 percent of the final mark.
Examination (internal/external)
Internal
Grading scale
7-scale
Exam date/dates
The written assignment is due for submission on the ... The written exam is set for the week 3
Deadline for withdrawal from exam
Wednesday, August 28, 2024
Academic responsibility and teachers
Academic responsibility
Herit Vivi Bentsdóttir Albinus
Teachers
Tróndur Møller Sandoy
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