Postgraduate Course: Foundations of Econometrics (CMSE11388)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Foundations of Econometrics provides a thorough training in basic econometric methods to enable students to critically assess applied work as well as to conduct their own research using appropriate econometric techniques. Together with Applications of Econometrics, this course will provide the background required to competently conduct quantitative research at the PhD level. |
| Course description |
This course will expose students to a thorough and rigorous treatment of basic econometric methods. Students will learn the assumptions of all basic estimators and the implications of these assumptions on the resulting estimates through mathematical proofs. Most of the material will focus on linear models; however, some nonlinear cases, such as limited dependent variable models and generalized methods of moments, will also be discussed. Examples in Finance and Economics will be used to provide context. This course serves as a foundation for the students' understanding of more advanced estimation techniques that will be delivered in Applications of Econometrics.
- An introduction to linear regression
- Interpreting and comparing regression models
- Heteroskedasticity and autocorrelation
- Endogenous regressor, instrumental variables and GMM
- Maximum likelihood estimation and specification tests
- Models with limited dependent variables
- Univariate time series models
- Multivariate time series models
- Models based on panel data
- Econometric techniques in practice
Formal teaching occurs in lectures and tutorials. However, much of the learning will be the result of students' own reading and reflection, and preparation for weekly quizzes and the final examination. Students will also be exposed to a commonly used statistical software (Stata) through independent learning.
A high level of student participation is expected, through discussion in class and among peers outside of class. Cooperation amongst students when completing exercises and during exam preparation is encouraged.
Students are assumed to have good background knowledge of matrix algebra as well as statistical and distribution theory.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | Permission from the Course Organiser to attend |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2018/19, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
156 )
|
| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Continuous assessment (50%) includes homework, quizzes and a presentation. Quizzes will take place at the beginning of tutorial classes. Questions can be multiple-choice or short answer and students will have 20 minutes to attempt the quiz. Problem sets will be provided as group homework assignments. The aim of quizzes and homework is to ensure that students are up to date with material. The last component, group presentation, requires the students to evaluate the empirical approach of an academic paper from a provided list, and give a presentation in front of their peers. The presentations will be held in the last week of class.«br /»
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The written examination (50%) will comprise two sections. The first section will focus on proofs, concepts, and theories. The second section will focus on interpretation and critical evaluation of empirical results. |
| Feedback |
Feedback will be provided in the form of solutions to tutorial questions, homework questions, and quizzes. Summative feedback will include specific feedback for individual groups on their coursework submissions and generic examination feedback. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | Foundations of Econometrics | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of basic econometric methods, their assumptions, and the consequences of the violations of these assumptions on empirical results
- Apply knowledge of basic econometrics to contemporary issues in Finance empirical research
- Critically evaluate econometric approaches currently used in Finance empirical research
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Reading List
Verbeek, M. 2012. A guide to modern econometrics. 4th ed. West Sussex: John Wiley & Sons.
Supplementary textbooks
Greene, W. H., 2012. Econometrics Analysis, 7th ed., NJ: Prentice Hall/Pearson.
Hayashi, F. 2000. Econometrics. NJ: Princeton University Press.
Stock, J. H., and Watson, M. M., 2012. Introduction to Econometrics. 3rd ed. London: Pearson
Education.
Note: The structure of the course and the notation used will follow Verbeek (2012), which is the core
textbook. Some proofs and presentations in lectures will be drawn from Hayashi (2000), particularly
when the links between GMM and other estimators are presented. Note that basic knowledge of
matrix algebra is required to understand these textbooks. Students can refresh their knowledge of
matrix algebra as well as foundation statistics through reading Appendices A and B of Verbeek (2012).
Additional readings will be suggested in the course of the lectures. They will mostly be examples of
how the methods presented in class are applied to empirical research in Finance. |
Additional Information
| Graduate Attributes and Skills |
Research and enquiry
Personal and intellectual autonomy
Personal effectiveness
Communication |
| Keywords | Econometrics,linear regression,linear models,finance |
Contacts
| Course organiser | Mr Ben Sila
Tel:
Email: Ben.Sila@ed.ac.uk |
Course secretary | Mrs Susan Keatinge
Tel: (0131 6)50 3810
Email: Susan.Keatinge@ed.ac.uk |
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