Introduction to Econometric Theory
The aim of this module is to help you understand the basic principles of econometric theory. The skills and knowledge acquired through this module will help you understand the foundation of econometrics and derive the major analytical results in econometrics. This module provides essential analytical tools for those intending to attend the third year module Econometric Analysis. It is also useful for those who wish to have a more theoretical understanding of econometrics.
Since mathematics is an international language the course content is relevant across the globe in theory and in practice.
Through the material mastered on this module, students learn numeracy skills and acquire advanced mathematical skills, which are highly valued by the majority of employers.
All of the resources for this module are available on the ELE (Exeter Learning Environment).
Full module specification
|Module title:||Introduction to Econometric Theory|
|Duration of module:||
Duration (weeks) - term 2: |
This module can be thought of as having two aims. The first is to complement the practical material taught in Statistics and Econometrics (BEE2006), for the benefit of more technically inclined and motivated students, by studying its underpinning in mathematics and statistical theory. The second aim is to provide an important preliminary coverage for those who will be taking Econometric Analysis (BEE3015) in year 3.
These are compulsory modules for those following the Economics with Econometrics degree. The module will focus largely on mastering matrix algebra, which is studied in the context of the multiple regression model and its ramifications. In parallel with this, the fundamentals of distribution theory are studied with a focus on the role of the normal distribution and the various distributions derived from it. These two topics come together and are integrated in the theory of statistical inference in the regression model.
ILO: Module-specific skills
- 1. analyse problems in econometrics using mathematically advanced techniques
- 2. explain the role of distribution theory in solving problems in statistical inference, with the multiple regression model providing the framework of analysis
ILO: Discipline-specific skills
- 3. demonstrate a clear understanding of the mathematical and statistical background of applied Economics
ILO: Personal and key skills
- 4. demonstrate the appropriate use of relevant mathematical language and methods in economics
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Homework assignments||Weekly problem sets||1,2||In-class discussions|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Examination||100||2 hours||1,2,3,4||Written feedback|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Examination||Examination||1, 2, 3, 4||August Examination Period|
• Observations, variables and summary statistics; the two-variable regression model; the multiple regression model. Basic matrix concepts and properties
• Solving systems of equations; determinants and inverse matrices; linear dependence and rank. More on multiple regression
• Probability distributions; the normal (Gaussian) distribution and its properties; the multivariate normal distribution; discrete distributions; continuous distributions derived from the normal: chi-squared , t, and F. Conditional distributions
• Statistical inference in the classical regression model; properties of least squares; the projection matrices; residual variance estimation; efficiency and the Gauss Markov theorem. Generalized least squares
• Eigenvalues, eigenvectors and diagonalization; the distribution of quadratic forms; OLS confidence regions; the t test; tests of linear restrictions. Constrained least squares.
• The partitioned regression model. Frisch-Waugh Theorem, specification analysis
• Random regressors; conditional expectations; properties of OLS with random regressors; the Gauss Markov theorem with random regressors;
• Asymptotic theory (a non-technical introduction).
• Maximum likelihood estimation.
Indicative learning resources - Basic reading
The course will not follow any single text. Lecture notes will be provided as the focus for study, and a
number of alternative textbooks are recommended for background reading.
Johnston, J. and DiNardo, J. (1997) Econometric Methods, 4th Edition, McGraw Hill
Theil, H. (1971) Principles of Econometrics, Wiley
Judge, G. et al, (1998) The Theory and Practice of Econometrics, 2nd Edition, Wiley
Greene, W. (2012) Econometric Analysis, 7th Edition, Pearson Education/Prentice Hall
Davidson, J. (2000) Econometric Theory, Oxford: Blackwell
Davidson, R. and MacKinnon J. (2003) Econometric Theory and Methods, New York: OUP
Module has an active ELE page?
Last revision date