Econometric Theory I
| Module title | Econometric Theory I |
|---|---|
| Module code | BEEM139 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Sebastian Kripfganz (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 12 |
|---|
Module description
Econometrics is the branch of economics devoted to the development and application of statistical methods to the study and clarification of the economic phenomena. This module provides an advanced introduction to econometric theory, understood as a collection of mathematical and statistical concepts and principles which motivates much of the empirical analysis conducted by economists.
Module aims - intentions of the module
This module aims at providing the students with an introduction to the theory of econometrics covering important topics in econometric estimation and inference. The module will provide students with the necessary knowledge for understanding recent developments in econometrics.
The module will help students carry out future applied econometric analysis by allowing them to understand the underlying econometric theory, which is important in order to make reasonable judgments about the methods to implement.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. explain theoretical aspects of important topics in econometrics
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 2. apply important general principles and tools used in econometrics
- 3. demonstrate a specialised knowledge of theoretical aspects of econometrics to enable you to carry out applied research or direct you towards an academic career
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 4. demonstrate a logical attitude towards the solution of problems
- 5. demonstrate confidence in identifying, tackling and solving research problems
Syllabus plan
The following non-exclusive list gives an indication about possible topics covered by this module:
1. The linear regression model
-Ordinary least squares estimation
-Unbiasedness and efficiency
2. Model specification and hypothesis testing
-The partitioned regression model
-Omitted variables and irrelevant regressors
-Testing linear restrictions
3. Asymptotic theory
-Modes of convergence
-Consistency and asymptotic normality
4. Instrumental variables
-Endogenous regressors
-Two-stage least squares estimation
-Generalised method of moments
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 32 | 118 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 22 | Lectures |
| Scheduled Learning and Teaching | 10 | Tutorials |
| Independent Study | 118 | Independent study |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Bi-weekly exercises | 1-6 questions | 1-5 | Oral/written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 30 | 70 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Problem Set 1 | 15 | 2-4 problems | 1-5 | Oral/Written |
| Problem Set 2 | 15 | 2-4 problems | 1-5 | Oral/Written |
| Final Exam | 70 | 2 hours | 1-5 | Oral/Written |
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 |
|---|---|---|---|
| Problem Set 1 | Problem Set 1 (15%) | 1-5 | August |
| Problem Set 2 | Problem Set 2 (15%) | 1-5 | August |
| Final exam | Final exam (70%) | 1-5 | August |
Indicative learning resources - Basic reading
Davidson, J. (2018). An introduction to econometric theory. Wiley.
Greene, W. H. (2012). Econometric analysis. Pearson.
Hansen, B. (2022). Probability and statistics for Economists. Princeton University Press.
Hansen, B. (2022). Econometrics. Princeton University Press.
Hayashi, F. (2000). Econometrics. Princeton University Press.
Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. MIT Press.
Wooldridge, J. M. (2020). Introductory econometrics: a modern approach. Cengage Learning.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | Only available to MRes Economics PhD pathway students |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 04/04/2016 |
| Last revision date | 13/02/2023 |
