Postgraduate Course: Foundations of Econometrics (CMSE11388)
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||Foundations of Econometrics aims to provide a thorough training in basic econometric methods, to enable students to critically assess applied work and conduct their own quantitative research using appropriate econometric techniques. Together with Applications of Econometrics, this course provides the foundations required to conduct quantitative research at the PhD level.
This course exposes students to a thorough and rigorous treatment of basic econometric methods. Students learn the assumptions and properties of basic estimators 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, are also 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
- Models based on panel data
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 assignments, weekly class tests, 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 strongly encouraged.
Students are assumed to have good background knowledge of matrix algebra as well as statistical and distribution theory.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|Pre-requisites||Permission from the Course Organiser to attend
|High Demand Course?
Course Delivery Information
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Seminar/Tutorial Hours 18,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||This course has 3 methods of assessment:
1. Individual Weekly Class Tests (20%)
2. Individual Report (10%)
3. Individual Written Examination (70%)
||Formative feedback: TBC
Summative feedback: will include specific feedback for individual groups on their coursework submissions and generic examination feedback.
||Hours & Minutes
|Main Exam Diet S1 (December)||Foundations of Econometrics||2:00|
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
|Verbeek, M. 2012. A guide to modern econometrics. 4th ed. West Sussex: John Wiley & Sons.|
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.
|Graduate Attributes and Skills
||Research and enquiry
Personal and intellectual autonomy
|Keywords||Econometrics,linear regression,linear models,finance
|Course organiser||Mr Ben Sila
|Course secretary||Mr Ciaran Masson
Tel: (0131 6)50 9945