Applied Econometrics 1
| Module title | Applied Econometrics 1 |
|---|---|
| Module code | BEEM011 |
| Academic year | 2020/1 |
| Credits | 15 |
| Module staff | Dr Arlan Brucal (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 9 |
| Number students taking module (anticipated) | 84 |
|---|
Module description
Many decisions in economics, business, and government rely on understanding relationships among variables around us. These relationships may include, for instance, price and consumption of fuel or inflation and unemployment. Economic theory provides clues about the direction of these relationships – for example fuel consumption should go down when price goes up – but the actual magnitude of the change must be learned empirically by analysing real-world data. Applied econometrics employs statistical methods to these data to give a numerical description of the relationship and a measure of how precise this numerical description is. In this module, we will briefly review probability theory and fundamental statistics, which will cover topics like hypothesis testing and confidence intervals. We will then proceed to regression analysis, which is the workhorse of applied econometrics. We will also attempt to cover more advanced topics in regression analysis such as panel data methods and instrumental variable regression.
Module aims - intentions of the module
The module aims to provide students with an applied econometric foundation necessary in order to conduct a high-standard empirical analysis of economic data.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Demonstrate aptitude in the econometric techniques to analyse economic data.
- 2. Exhibit technical expertise to analyse the data in R or STATA using different econometric software packages.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. Formulate hypotheses of interest, derive the necessary tools to test these hypotheses and interpret the results.
- 4. Demonstrate a specialised knowledge of linking the theory and empirical questions.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 5. Solve the analytical problems and provide appropriate interpretation of the outcomes for decision making.
- 6. Demonstrate data analysis skills.
Syllabus plan
The syllabus plan is as follows:
- Review of probability and statistics
- Fundamentals of regression analysis
- Further topics in Regression Analysis
- Regression with panel data
- Instrumental Variables Regression
The convenor and the university reserve the right to modify elements of the course during the term. It is the responsibility of the student to check his/her email and course websites weekly during the term to note any changes.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 36 | 114 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching activities | 36 | Lectures (9 x 2 hours) and Tutorials (9 x 2 hour) |
| Guided independent study | 40 | Writing up reports from empirical analysis of real data |
| Guided independent study | 34 | Reading and research |
| Guided independent study | 40 | Learning and practising the econometric software package |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Weekly exercises | 3-5 questions | 1-6 | Verbal/Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| 4 written assignments (25% each) | 100 | 1000 words maximum each | 1-6 | 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 |
|---|---|---|---|
| 4 written assignments (1000 words maximum each, 25% each) 100% | 4 written assignments (1000 words maximum each, 25% each) 100% | 1-6 | August reassessment period |
Indicative learning resources - Basic reading
There is no set text for this module. The lecture notes will be self-contained and available. Nonetheless resources that are useful reference to study methods in this course include the following:
Introduction to Econometrics by James Stock and Mark Watson, 4th Edition, 2015 (Pearson International)
Introduction to Econometrics with R by Christoph Hanck, Martin Arnold, Alexander Gerber and Martin Schmelzer (2019) – accessible at https://www.econometrics-with-r.org/index.html. This book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Basic Econometrics by Damodar N. Gujarati, 2009 (McGram Hill),
Introduction to Econometrics by Christopher Dougherty, 2016 (Oxford),
Microeconometrics Using STATA, Revised Edition, 2010 (Stata Press)
Discovering statistics using R by Andy Field, Jeremy Miles and Zoe Field, 2012 (Sage),
Indicative learning resources - Other resources
R, R Studio or STATA
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 29/09/2016 |
| Last revision date | 08/09/2020 |
