Econometrics
Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management
Teaching language: English
EKA: B580001102
Censorship: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Bachelor
Course ID: B580001101
ECTS value: 10
Date of Approval: 05-10-2021
Duration: 1 semester
Course ID
Course Title
Teaching language
ECTS value
Responsible study board
Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management
Date of Approval
Course Responsible
Offered in
Level
Offered in
Duration
Recommended prerequisites
Students in this course should have passed the following courses or similar: Mathematics (B540006101), Statistics (B540023101), and Regression Analysis ( B540009101). In addition Students should have a basic knowledge of the software R. It is expected that students have an understanding of basic probability theory, conditional and joint distribution functions, event probabilities, expectations and conditional expectations, basic inference and hypothesis testing. It is also expected that students are proficient in calculus and can compute standard derivatives and integrals. Some knowledge of matrix algebra is recommended but not required.
Aim and purpose
The purpose of this course is to acquire fundamental knowledge and skills in econometrics analysis. Students will develop an understanding of a broad range of empirical models and the practical skills needed to implement them in practice. The course builds on familiarity with the basic regression model, and relies heavily on an understanding of concepts developed in the Statistics course. Students will also see many of the topics from "Mathematics" put to use in practice. This course will also prepare students to follow the master courses "Microeconometrics" and "Topics in Econometrics".
Content
The main focus in the course is on the basic multiple regression model and deviations from the classical model assumptions. We will cover the both the consequences of - and solutions to violated assumptions. We go through both cross-sectional analysis, time series analysis and panel data analysis. In addition, we will cover some useful non-linear regression models. Throughout, we maintain a strong focus on model specification, properties of estimators, estimation, and hypothesis testing.
Description of outcome - Knowledge
After the course, the student should be familiar with basic econometric theory and methods for cross section and times series data.
Description of outcome - Skills
The student should be able to:
- Explain the main distinction between causality and correlation.
- Explain and implement a variety of estimation methods (Least squares, Maximum Likelihood, Least absolute deviations, quantile regression, the bootstrap).
- Properly choose between estimators (Ordinary Least Squares (OLS), Weighted Least Squares etc.)
- Explain the algebraic and statistical properties of the estimators.
- Perform valid inference in a variety of circumstances.
- Understand issues of misspecification and reflect on consequences thereof.
- Understand differences in interpretation and requirements of models applied to cross-sectional and time series data.
Description of outcome - Competences
After the course, the student should be able to plan, perform and discuss econometric analyses, based on cross section and times series data.
Literature
- Heij C, de Boer P, Franses PH, Kloek T, van Dijk H. Econometric Methods with Applications in Business and Economics, latest edition, Oxford University Press.
- Enders: Applied Econometrics Time Series, latest edition, Wiley
- Various additional materials.
- The software used in the course is R (free, open source).
Teaching Method
In order to achieve the learning objectives of the course, teaching will be organized in lectures and exercise tutorials. Lectures seek to introduce students to knowledge through notions and concepts, and to support their learning process. The purpose of the exercise tutorials is to foster and support the development of students’ skills in applying this knowledge to concrete problems. In order to make the most of the course, students should aim to attend all lectures. Students are expected to come to class having read the required chapters and prepared to discuss them.
Weekly exercise sessions aim to provide students with hands-on experience with analysing real world economic data using statistical software. In order to make the most of these sessions, students should aim to attend them all having solved the assigned questions beforehand.
Workload
Scheduled classes:
4 (2x2) lectures and 2 exercises weekly for 13 weeks.
Workload:
The students' workload is expected to be distributed as follows:
Lectures - 52 hours
Exercises - 26 hours
Preparation - 162 hours
Exam preparation - 30 hours
Total: 270 hours.
This corresponds to an average weekly workload of 13 hours during the semester, including the exam.
Examination regulations
Written examination.
Name
Written examination.
Timing
Exam: June
Reexam: August
Tests
Exam
Name
Exam
Form of examination
Written examination on premises
Censorship
Second examiner: Internal
Grading
7-point grading scale
Identification
Student Identification Card - Exam number
Language
English
Duration
3 hours.
Length
No limitations.
Examination aids
All exam aids allowed.
Assignment handover
The assignment is handed over in Digital Exam.
Assignment handin
Electronic hand-in via Digital Exam.
ECTS value
10
Additional information
-
EKA
B580001102
External comment
NOTE - This course is identical with the former course B580001101 Økonometri.
Examination in this course is not allowed if the student has passed the following course:
Økonometri (course no. B580001101/B580001102).
Used examination attempts in the former identical course will be transferred.
Courses that are identical with former courses that are passed according to applied rules cannot be retaken.
Courses offered
| Offer period | Offer type | Profile | Education | Semester |
|---|---|---|---|---|
| Spring 2024 | Mandatory | Bacheloruddannelsen i Matematik-Økonomi, Odense, gældende fra 1. september 2020 | BSc in Mathematics-Economics - 2022 | Bachelor of Science in Mathematics-Economics | Odense | 6 |
| Spring 2024 | Mandatory | Fra 1. september 2023 optages der ikke længere studerende på denne linje - Bacheloruddannelsen i Økonomi - Erhvervsøkonomisk linje, Odense, gældende fra 1. september 2020 | BSc in Economics - 2022 | Bachelor of Science in Economics | Odense | 4 |
| Spring 2024 | Mandatory | Fra 1. september 2023 optages der ikke længere studerende på denne linje - Bacheloruddannelsen i Økonomi - Samfundsøkonomisk linje, Odense, gældende fra 1. september 2020 | BSc in Economics - 2022 | Bachelor of Science in Economics | Odense | 4 | Spring 2024 | Exchange students |
Teachers
| Name | Department | City | |
|---|---|---|---|
| Jørgen T Lauridsen | jtl@sam.sdu.dk | Econometrics and Data Science | Odense |
| Volha Lazuka | vola@sam.sdu.dk | Econometrics and Data Science | Odense |